[
  {
    "tag": "regret learning",
    "mean_rating": 6.74,
    "n_papers": 10,
    "p90_rating": 7.4,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Tractable Multi-Agent Reinforcement Learning through Behavioral Economics",
        "paper_id": "openreview:stUKwWBuBm",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Honor Among Bandits: No-Regret Learning for Online Fair Division",
        "paper_id": "openreview:OCQbC0eDJJ",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank",
        "paper_id": "openreview:AOlm45AUVS",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "No-regret Learning in Harmonic Games: Extrapolation in the Face of Conflicting Interests",
        "paper_id": "openreview:HW9S9vY5gZ",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "The Equivalence of Dynamic and Strategic Stability under Regularized Learning in Games",
        "paper_id": "openreview:nCLdsEzZBV",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "optimal sample complexity",
    "mean_rating": 6.45,
    "n_papers": 13,
    "p90_rating": 7.5,
    "samples": [
      {
        "avg_rating": 7.75,
        "title": "Span-Based Optimal Sample Complexity for Weakly Communicating and General Average Reward MDPs",
        "paper_id": "openreview:pGEY8JQ3qx",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem",
        "paper_id": "openreview:HgOJlxzB16",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Optimal Sample Complexity of Contrastive Learning",
        "paper_id": "openreview:NU9AYHJvYe",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models",
        "paper_id": "openreview:73XPopmbXH",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Optimal Sample Complexity for Average Reward Markov Decision Processes",
        "paper_id": "openreview:jOm5p3q7c7",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "independent interest",
    "mean_rating": 6.3,
    "n_papers": 12,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem",
        "paper_id": "openreview:TQlpqmCeMe",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits",
        "paper_id": "openreview:rakhNY32vw",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Quasi-Monte Carlo Graph Random Features",
        "paper_id": "openreview:zCFfv49MjE",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "When Is Inductive Inference Possible?",
        "paper_id": "openreview:2aGcshccuV",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Randomized and Deterministic Maximin-share Approximations for Fractionally Subadditive Valuations",
        "paper_id": "openreview:I3k2NHt1zu",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "error rate",
    "mean_rating": 6.29,
    "n_papers": 13,
    "p90_rating": 6.96,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Universal Rates for Active Learning",
        "paper_id": "openreview:T0e4Nw09XX",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets",
        "paper_id": "openreview:8Gqz2opok1",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.8,
        "title": "When are ensembles really effective?",
        "paper_id": "openreview:jS4DUGOtBD",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust",
        "paper_id": "openreview:4NQ24cHnOi",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "How many classifiers do we need?",
        "paper_id": "openreview:m5dyKArVn8",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "convex functions",
    "mean_rating": 6.28,
    "n_papers": 12,
    "p90_rating": 7.22,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Tight Lower Bounds under Asymmetric High-Order H\u00f6lder Smoothness and Uniform Convexity",
        "paper_id": "openreview:fMTPkDEhLQ",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks",
        "paper_id": "openreview:IUKff7nYmW",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Difference-of-submodular Bregman Divergence",
        "paper_id": "openreview:vr1QdCNJmN",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search",
        "paper_id": "openreview:mkzpN2T87C",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Gradient correlation is a key ingredient to accelerate SGD with momentum",
        "paper_id": "openreview:2Q8gTck8Uq",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "task arithmetic",
    "mean_rating": 6.27,
    "n_papers": 13,
    "p90_rating": 7.4,
    "samples": [
      {
        "avg_rating": 8.2,
        "title": "Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models",
        "paper_id": "openreview:0A9f2jZDGW",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.5,
        "title": "When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers",
        "paper_id": "openreview:vRvVVb0NAz",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Parameter-Efficient Multi-Task Model Fusion with Partial Linearization",
        "paper_id": "openreview:iynRvVVAmH",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "AdaMerging: Adaptive Model Merging for Multi-Task Learning",
        "paper_id": "openreview:nZP6NgD3QY",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic",
        "paper_id": "openreview:dj0TktJcVI",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "star",
    "mean_rating": 6.26,
    "n_papers": 13,
    "p90_rating": 7.27,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Lean-STaR: Learning to Interleave Thinking and Proving",
        "paper_id": "openreview:SOWZ59UyNc",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation",
        "paper_id": "openreview:cYZibc2gKf",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "STAR: Synthesis of Tailored Architectures",
        "paper_id": "openreview:HsHxSN23rM",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.4,
        "title": "Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation",
        "paper_id": "openreview:Ct0zPIe3xs",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Stable Anisotropic Regularization",
        "paper_id": "openreview:dbQH9AOVd5",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "fundamental limitation",
    "mean_rating": 6.24,
    "n_papers": 12,
    "p90_rating": 7.22,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Backtracking Improves Generation Safety",
        "paper_id": "openreview:Bo62NeU6VF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds",
        "paper_id": "openreview:5FXKgOxmb2",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Improving Equivariant Networks with Probabilistic Symmetry Breaking",
        "paper_id": "openreview:ZE6lrLvATd",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Fundamental Limitation of Alignment in Large Language Models",
        "paper_id": "openreview:4qFIkOhq24",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "RRM:  Robust Reward Model Training Mitigates Reward Hacking",
        "paper_id": "openreview:88AS5MQnmC",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "practical usage",
    "mean_rating": 6.24,
    "n_papers": 10,
    "p90_rating": 7.6,
    "samples": [
      {
        "avg_rating": 8.5,
        "title": "DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation",
        "paper_id": "openreview:UyNXMqnN3c",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "A Simple Romance Between Multi-Exit Vision Transformer and Token Reduction",
        "paper_id": "openreview:gJeYtRuguR",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis",
        "paper_id": "openreview:eD534mPhAg",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "On the Role of General Function Approximation in Offline Reinforcement Learning",
        "paper_id": "openreview:JSS9rKHySk",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "PaLD: Detection of Text Partially Written by Large Language Models",
        "paper_id": "openreview:rWjZWHYPcz",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "reconstruction model",
    "mean_rating": 6.23,
    "n_papers": 12,
    "p90_rating": 7.93,
    "samples": [
      {
        "avg_rating": 8.5,
        "title": "LRM: Large Reconstruction Model for Single Image to 3D",
        "paper_id": "openreview:sllU8vvsFF",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction Model",
        "paper_id": "openreview:H4yQefeXhp",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Instant3D: Fast Text-to-3D with Sparse-view Generation and Large Reconstruction Model",
        "paper_id": "openreview:2lDQLiH1W4",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "RelitLRM: Generative Relightable Radiance for Large Reconstruction Models",
        "paper_id": "openreview:3Oli4u6q3p",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.25,
        "title": "MeshLRM: Large Reconstruction Model for High-Quality Meshes",
        "paper_id": "openreview:R1rNN22IoP",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "t$ rounds",
    "mean_rating": 6.23,
    "n_papers": 12,
    "p90_rating": 6.74,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Optimal Multiclass U-Calibration Error and Beyond",
        "paper_id": "openreview:7aFRgCC8Q7",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "On Stochastic Contextual Bandits with Knapsacks in Small Budget Regime",
        "paper_id": "openreview:FCMpUOZkxi",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.6,
        "title": "No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions",
        "paper_id": "openreview:0WLMVDdvDF",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting",
        "paper_id": "openreview:WqNfihAcu5",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Fair Allocation in Dynamic Mechanism Design",
        "paper_id": "openreview:bEunGps83o",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "order algorithm",
    "mean_rating": 6.22,
    "n_papers": 11,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization",
        "paper_id": "openreview:tuu4de7HL1",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "A Fast and Provable Algorithm for Sparse Phase Retrieval",
        "paper_id": "openreview:BlkxbI6vzl",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Post-processing Private Synthetic Data for Improving Utility on Selected Measures",
        "paper_id": "openreview:neu9JlNweE",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Private Zeroth-Order Nonsmooth Nonconvex Optimization",
        "paper_id": "openreview:IzqZbNMZ0M",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Second-Order Algorithms for Finding Local Nash Equilibria in Zero-Sum Games",
        "paper_id": "openreview:dug02AimLZ",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "label distribution",
    "mean_rating": 6.22,
    "n_papers": 10,
    "p90_rating": 7.64,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Learning to Reject Meets Long-tail Learning",
        "paper_id": "openreview:ta26LtNq2r",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.6,
        "title": "Subgraph Federated Learning for Local Generalization",
        "paper_id": "openreview:cH65nS5sOz",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing",
        "paper_id": "openreview:cMUBkkTrMo",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition",
        "paper_id": "openreview:PaqJ71zf1M",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "OTTER: Effortless Label Distribution Adaptation of Zero-shot Models",
        "paper_id": "openreview:RsawwSBCs7",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "fundamental limits",
    "mean_rating": 6.21,
    "n_papers": 13,
    "p90_rating": 6.95,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods",
        "paper_id": "openreview:NgyT80IPUK",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models",
        "paper_id": "openreview:TeBKVfhP2M",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+\\alpha$ Moments",
        "paper_id": "openreview:mvSDs51eqQ",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "The Limits of Differential Privacy in Online Learning",
        "paper_id": "openreview:Cqr6E81iB7",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Towards the Fundamental Limits of Knowledge Transfer over Finite Domains",
        "paper_id": "openreview:Zh2iqiOtMt",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "visual cortex",
    "mean_rating": 6.2,
    "n_papers": 15,
    "p90_rating": 7.3,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Learning and aligning single-neuron invariance manifolds in visual cortex",
        "paper_id": "openreview:kbjJ9ZOakb",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Emergent Orientation Maps \u2014\u2014 Mechanisms, Coding Efficiency and Robustness",
        "paper_id": "openreview:rySLejeB1k",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Reproducibility of predictive networks for mouse visual cortex",
        "paper_id": "openreview:VXxj3XZ1X8",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.8,
        "title": "Brain-inspired $L_p$-Convolution benefits large kernels and aligns better with visual cortex",
        "paper_id": "openreview:jz35igczhm",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex",
        "paper_id": "openreview:X4mmXQ4Nxw",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "neural data",
    "mean_rating": 6.2,
    "n_papers": 10,
    "p90_rating": 7.5,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Estimating Shape Distances on Neural Representations with Limited Samples",
        "paper_id": "openreview:kvByNnMERu",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "In vivo cell-type and brain region classification via multimodal contrastive learning",
        "paper_id": "openreview:10JOlFIPjt",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Probabilistic Geometric Principal Component Analysis with application to neural data",
        "paper_id": "openreview:mkDam1xIzW",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)",
        "paper_id": "openreview:xkgfLXZ4e0",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.25,
        "title": "Differentiable Optimization of Similarity Scores Between Models and Brains",
        "paper_id": "openreview:vWRwdmA3wU",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "latent dynamics",
    "mean_rating": 6.2,
    "n_papers": 13,
    "p90_rating": 7.35,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Feedback Favors the Generalization of Neural ODEs",
        "paper_id": "openreview:cmfyMV45XO",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Meta-Dynamical State Space Models for Integrative Neural Data Analysis",
        "paper_id": "openreview:SRpq5OBpED",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt",
        "paper_id": "openreview:Dl3MsjaIdp",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity",
        "paper_id": "openreview:qf2uZAdy1N",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Parsing neural dynamics with infinite recurrent switching linear dynamical systems",
        "paper_id": "openreview:YIls9HEa52",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "stein variational gradient descent",
    "mean_rating": 6.18,
    "n_papers": 13,
    "p90_rating": 6.9,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent",
        "paper_id": "openreview:sbG8qhMjkZ",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Learning Rate Free Sampling in Constrained Domains",
        "paper_id": "openreview:TNAGFUcSP7",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation",
        "paper_id": "openreview:DBz9E5aZey",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "ELBOing Stein: Variational Bayes with Stein Mixture Inference",
        "paper_id": "openreview:2rBLbNJwBm",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "A Finite-Particle Convergence Rate for Stein Variational Gradient Descent",
        "paper_id": "openreview:0eRDQQK2TW",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "nash equilibria",
    "mean_rating": 6.18,
    "n_papers": 19,
    "p90_rating": 8.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization",
        "paper_id": "openreview:cc8h3I3V4E",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "Tractable Multi-Agent Reinforcement Learning through Behavioral Economics",
        "paper_id": "openreview:stUKwWBuBm",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "The Complexity of Two-Team Polymatrix Games with Independent Adversaries",
        "paper_id": "openreview:9VGTk2NYjF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "On Tractable $\\Phi$-Equilibria in Non-Concave Games",
        "paper_id": "openreview:3CtTMF5zzM",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.8,
        "title": "Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity",
        "paper_id": "openreview:0Iw2dLh8uq",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "theorem proving",
    "mean_rating": 6.18,
    "n_papers": 10,
    "p90_rating": 7.55,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Magnushammer: A Transformer-Based Approach to Premise Selection",
        "paper_id": "openreview:oYjPk8mqAV",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Lean-STaR: Learning to Interleave Thinking and Proving",
        "paper_id": "openreview:SOWZ59UyNc",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "LEGO-Prover: Neural Theorem Proving with Growing Libraries",
        "paper_id": "openreview:3f5PALef5B",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data",
        "paper_id": "openreview:8xliOUg9EW",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Beyond Autoregression: Fast LLMs via Self-Distillation Through Time",
        "paper_id": "openreview:uZ5K4HeNwd",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "level concepts",
    "mean_rating": 6.17,
    "n_papers": 13,
    "p90_rating": 7.4,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models",
        "paper_id": "openreview:3bq3jsvcQ1",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Learning to Receive Help: Intervention-Aware Concept Embedding Models",
        "paper_id": "openreview:4ImZxqmT1K",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition",
        "paper_id": "openreview:g1fkhbhHjL",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts",
        "paper_id": "openreview:tLTtqySDFb",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?",
        "paper_id": "openreview:5oJlyJXUxK",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "simple model",
    "mean_rating": 6.17,
    "n_papers": 11,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Language Representations Can be What Recommenders Need: Findings and Potentials",
        "paper_id": "openreview:eIJfOIMN9z",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds",
        "paper_id": "openreview:txD9llAYn9",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "The Pursuit of Human Labeling: A New Perspective on Unsupervised Learning",
        "paper_id": "openreview:3GpIeVYw8X",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities",
        "paper_id": "openreview:8PWvdaRQAu",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "TabM: Advancing tabular deep learning with parameter-efficient ensembling",
        "paper_id": "openreview:Sd4wYYOhmY",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "kernel ridge regression",
    "mean_rating": 6.16,
    "n_papers": 11,
    "p90_rating": 7.4,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Generalization error of spectral algorithms",
        "paper_id": "openreview:3SJE1WLB4M",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.4,
        "title": "Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels",
        "paper_id": "openreview:OwpLQrpdwE",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "The Exact Sample Complexity Gain from Invariances for Kernel Regression",
        "paper_id": "openreview:6iouUxI45W",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "On the Saturation Effects of Spectral Algorithms in Large Dimensions",
        "paper_id": "openreview:kJzecLYsRi",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay",
        "paper_id": "openreview:E4P5kVSKlT",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "neural responses",
    "mean_rating": 6.16,
    "n_papers": 15,
    "p90_rating": 7.15,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Comparing noisy neural population dynamics using optimal transport distances",
        "paper_id": "openreview:cNmu0hZ4CL",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT",
        "paper_id": "openreview:AiMs8GPP5q",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Modeling dynamic social vision highlights gaps between deep learning and humans",
        "paper_id": "openreview:wAXsx2MYgV",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex",
        "paper_id": "openreview:X4mmXQ4Nxw",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Multimodal Deep Learning Model Unveils Behavioral Dynamics of V1 Activity in Freely Moving Mice",
        "paper_id": "openreview:qv5UZJTNda",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "differential equation",
    "mean_rating": 6.16,
    "n_papers": 10,
    "p90_rating": 6.55,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Understanding Optimization in Deep Learning with Central Flows",
        "paper_id": "openreview:sIE2rI3ZPs",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit",
        "paper_id": "openreview:PqfPjS9JRX",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation",
        "paper_id": "openreview:uwO71a8wET",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Learning Distributions on Manifolds with Free-Form Flows",
        "paper_id": "openreview:QbPHYPZKJI",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.2,
        "title": "Diffusion Bridge Implicit Models",
        "paper_id": "openreview:eghAocvqBk",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "unknown distribution",
    "mean_rating": 6.16,
    "n_papers": 14,
    "p90_rating": 6.96,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation",
        "paper_id": "openreview:lsxeNvYqCj",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Private Distribution Learning with Public Data: The View from Sample Compression",
        "paper_id": "openreview:nDIrJmKPd5",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.857142857142857,
        "title": "How to Verify Any (Reasonable) Distribution Property: Computationally Sound Argument Systems for Distributions",
        "paper_id": "openreview:GfXMTAJaxZ",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits",
        "paper_id": "openreview:LROEcjVkv5",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Adaptive Data Analysis in a Balanced Adversarial Model",
        "paper_id": "openreview:QatZNssk7T",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "finite number",
    "mean_rating": 6.15,
    "n_papers": 19,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Honor Among Bandits: No-Regret Learning for Online Fair Division",
        "paper_id": "openreview:OCQbC0eDJJ",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "When Is Inductive Inference Possible?",
        "paper_id": "openreview:2aGcshccuV",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "HyPoGen: Optimization-Biased Hypernetworks for Generalizable Policy Generation",
        "paper_id": "openreview:CJWMXqAnAy",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Transformers are Universal In-context Learners",
        "paper_id": "openreview:6S4WQD1LZR",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Hypervolume Maximization: A Geometric View of Pareto Set Learning",
        "paper_id": "openreview:9ieV1hnuva",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "function class",
    "mean_rating": 6.15,
    "n_papers": 11,
    "p90_rating": 6.75,
    "samples": [
      {
        "avg_rating": 6.8,
        "title": "Oracle-Efficient Differentially Private Learning with Public Data",
        "paper_id": "openreview:BAjjINf0Oh",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Generalization Analysis for Label-Specific Representation Learning",
        "paper_id": "openreview:dtPIUXdJHY",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Debiased Collaborative Filtering with Kernel-Based Causal Balancing",
        "paper_id": "openreview:Ffjc8ApSbt",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "In-Context Learning through the Bayesian Prism",
        "paper_id": "openreview:HX5ujdsSon",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods",
        "paper_id": "openreview:GQ1Tc3vHbt",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "linear inverse problems",
    "mean_rating": 6.14,
    "n_papers": 11,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.5,
        "title": "Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems.",
        "paper_id": "openreview:nHESwXvxWK",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Conditional score-based diffusion models for Bayesian inference in infinite dimensions",
        "paper_id": "openreview:voG6nEW9BV",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems",
        "paper_id": "openreview:DsEhqQtfAG",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models",
        "paper_id": "openreview:XKBFdYwfRo",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Diffusion Posterior Sampling for Linear Inverse Problem Solving: A Filtering Perspective",
        "paper_id": "openreview:tplXNcHZs1",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "layer neural networks",
    "mean_rating": 6.14,
    "n_papers": 34,
    "p90_rating": 7.23,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality",
        "paper_id": "openreview:4xWQS2z77v",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.666666666666667,
        "title": "High-dimensional SGD aligns with emerging outlier eigenspaces",
        "paper_id": "openreview:MHjigVnI04",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem",
        "paper_id": "openreview:HgOJlxzB16",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Towards a Unified and Verified Understanding of Group-Operation Networks",
        "paper_id": "openreview:8xxEBAtD7y",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks",
        "paper_id": "openreview:fShubymWrc",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "rigorous analysis",
    "mean_rating": 6.14,
    "n_papers": 10,
    "p90_rating": 6.65,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Noisy Interpolation Learning with Shallow Univariate ReLU Networks",
        "paper_id": "openreview:GTUoTJXPBf",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Ensemble sampling for linear bandits: small ensembles suffice",
        "paper_id": "openreview:SO7fnIFq0o",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Hardness of Learning Neural Networks under the Manifold Hypothesis",
        "paper_id": "openreview:dkkgKzMni7",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm",
        "paper_id": "openreview:DNHGKeOhLl",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors",
        "paper_id": "openreview:hQvX9MBowC",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "cell types",
    "mean_rating": 6.14,
    "n_papers": 10,
    "p90_rating": 7.55,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "When Selection Meets Intervention: Additional Complexities in Causal Discovery",
        "paper_id": "openreview:xByvdb3DCm",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics",
        "paper_id": "openreview:dsHpulHpOK",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization",
        "paper_id": "openreview:F8DWffLkYG",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Cell ontology guided transcriptome foundation model",
        "paper_id": "openreview:aeYNVtTo7o",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems",
        "paper_id": "openreview:9sP4oejtjB",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "lower bounds",
    "mean_rating": 6.13,
    "n_papers": 71,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Estimating Shape Distances on Neural Representations with Limited Samples",
        "paper_id": "openreview:kvByNnMERu",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Precise asymptotic generalization for multiclass classification with overparameterized linear models",
        "paper_id": "openreview:cRGINXQWem",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.25,
        "title": "Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks",
        "paper_id": "openreview:IUKff7nYmW",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "On the Minimax Regret for Online Learning with Feedback Graphs",
        "paper_id": "openreview:XfYpIaKDb6",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics",
        "paper_id": "openreview:DZcmz9wU0i",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "generalization analysis",
    "mean_rating": 6.13,
    "n_papers": 10,
    "p90_rating": 7.05,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers",
        "paper_id": "openreview:vRvVVb0NAz",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning",
        "paper_id": "openreview:UAow2kPsYP",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "Generalization Analysis for Label-Specific Representation Learning",
        "paper_id": "openreview:dtPIUXdJHY",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Stability and Generalization of Adversarial Training for Shallow Neural Networks with Smooth Activation",
        "paper_id": "openreview:9Nsa4lVZeD",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.142857142857143,
        "title": "Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization",
        "paper_id": "openreview:0ycX03sMAT",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "neural fields",
    "mean_rating": 6.13,
    "n_papers": 21,
    "p90_rating": 8.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "ResFields: Residual Neural Fields for Spatiotemporal Signals",
        "paper_id": "openreview:EHrvRNs2Y0",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling",
        "paper_id": "openreview:Fur0DtynPX",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields",
        "paper_id": "openreview:5UKrnKuspb",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "OmniRe: Omni Urban Scene Reconstruction",
        "paper_id": "openreview:11xgiMEI5o",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision",
        "paper_id": "openreview:ycv2z8TYur",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "iterate convergence",
    "mean_rating": 6.12,
    "n_papers": 23,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Divergence-Regularized Discounted Aggregation: Equilibrium Finding in Multiplayer Partially Observable Stochastic Games",
        "paper_id": "openreview:KD5nJUgeW4",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games",
        "paper_id": "openreview:LWeVVPuIx0",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Fast Last-Iterate Convergence of Learning in Games Requires Forgetful Algorithms",
        "paper_id": "openreview:hK7XTpCtBi",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Last Iterate Convergence of Incremental Methods as a Model of Forgetting",
        "paper_id": "openreview:mSGcDhQPwm",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Single-agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning",
        "paper_id": "openreview:46xYl55hdc",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "human motion",
    "mean_rating": 6.12,
    "n_papers": 15,
    "p90_rating": 7.33,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency",
        "paper_id": "openreview:weM4YBicIP",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "H-GAP: Humanoid Control with a Generalist Planner",
        "paper_id": "openreview:LYG6tBlEX0",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Following the Human Thread in Social Navigation",
        "paper_id": "openreview:M8OGl34Pmg",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Towards Unified Human Motion-Language Understanding via Sparse Interpretable Characterization",
        "paper_id": "openreview:Oh8MuCacJW",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "MoMu-Diffusion: On Learning Long-Term Motion-Music Synchronization and Correspondence",
        "paper_id": "openreview:YscR3LBIi7",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "provable guarantees",
    "mean_rating": 6.12,
    "n_papers": 32,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement",
        "paper_id": "openreview:UHPnqSTBPO",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization",
        "paper_id": "openreview:cc8h3I3V4E",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "Abstracting and Refining Provably Sufficient Explanations of Neural Network Predictions",
        "paper_id": "openreview:1IeCqgULIM",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees",
        "paper_id": "openreview:RMgqvQGTwH",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks",
        "paper_id": "openreview:fShubymWrc",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "minimax optimization",
    "mean_rating": 6.12,
    "n_papers": 13,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.2,
        "title": "DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining",
        "paper_id": "openreview:lXuByUeHhd",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization",
        "paper_id": "openreview:hCdqDkA25J",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Continuous-time Analysis of Anchor Acceleration",
        "paper_id": "openreview:rN99gLCBe4",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems",
        "paper_id": "openreview:YItWKZci78",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization",
        "paper_id": "openreview:NVDYgEFXCy",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "total variation distance",
    "mean_rating": 6.12,
    "n_papers": 14,
    "p90_rating": 6.78,
    "samples": [
      {
        "avg_rating": 7.2,
        "title": "Computational Explorations of Total Variation Distance",
        "paper_id": "openreview:xak8c9l1nu",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.8,
        "title": "Uncertainty Quantification via Stable Distribution Propagation",
        "paper_id": "openreview:cZttUMTiPL",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "The adaptive complexity of parallelized log-concave sampling",
        "paper_id": "openreview:EeqlkPpaV8",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Wasserstein-Regularized Conformal Prediction under General Distribution Shift",
        "paper_id": "openreview:aJ3tiX1Tu4",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel",
        "paper_id": "openreview:MT3aOfXIbY",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "conditional average treatment effect",
    "mean_rating": 6.12,
    "n_papers": 11,
    "p90_rating": 7.25,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation",
        "paper_id": "openreview:d3xKPQVjSc",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation",
        "paper_id": "openreview:yuy6cGt3KL",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators",
        "paper_id": "openreview:k4EP46Q9X2",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Conformal Meta-learners for Predictive Inference of Individual Treatment Effects",
        "paper_id": "openreview:IwnINorSZ5",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Differentially private learners for heterogeneous treatment effects",
        "paper_id": "openreview:1z3SOCwst9",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "intensive tasks",
    "mean_rating": 6.11,
    "n_papers": 15,
    "p90_rating": 8.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Inference Scaling for Long-Context Retrieval Augmented Generation",
        "paper_id": "openreview:FSjIrOm1vz",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models",
        "paper_id": "openreview:WbWtOYIzIK",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models",
        "paper_id": "openreview:3bq3jsvcQ1",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Retrieval is Accurate Generation",
        "paper_id": "openreview:oXYZJXDdo7",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Mixture of Parrots: Experts improve memorization more than reasoning",
        "paper_id": "openreview:9XETcRsufZ",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "elbo",
    "mean_rating": 6.11,
    "n_papers": 16,
    "p90_rating": 7.7,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series",
        "paper_id": "openreview:8zJRon6k5v",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "Progressive Compression with Universally Quantized Diffusion Models",
        "paper_id": "openreview:CxXGvKRDnL",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.4,
        "title": "Understanding Diffusion Objectives as the ELBO with Simple Data Augmentation",
        "paper_id": "openreview:NnMEadcdyD",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.25,
        "title": "Particle Semi-Implicit Variational Inference",
        "paper_id": "openreview:p3gMGkHMkM",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Diffusion Models With Learned Adaptive Noise",
        "paper_id": "openreview:loMa99A4p8",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "free reinforcement learning",
    "mean_rating": 6.11,
    "n_papers": 14,
    "p90_rating": 7.35,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Interpreting Emergent Planning in Model-Free Reinforcement Learning",
        "paper_id": "openreview:DzGe40glxs",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Towards General-Purpose Model-Free Reinforcement Learning",
        "paper_id": "openreview:R1hIXdST22",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning",
        "paper_id": "openreview:vNiI3aGcE6",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time",
        "paper_id": "openreview:nFsbQHFmj2",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "On Representation Complexity of Model-based and Model-free Reinforcement Learning",
        "paper_id": "openreview:3K3s9qxSn7",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "research efforts",
    "mean_rating": 6.11,
    "n_papers": 13,
    "p90_rating": 6.95,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram",
        "paper_id": "openreview:WcOohbsF4H",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Unveiling the Pitfalls of Knowledge Editing for Large Language Models",
        "paper_id": "openreview:fNktD3ib16",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Optimistic Bayesian Optimization with Unknown Constraints",
        "paper_id": "openreview:D4NJFfrqoq",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series",
        "paper_id": "openreview:3O5YCEWETq",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering",
        "paper_id": "openreview:aIMi2lOKIn",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "benign overfitting",
    "mean_rating": 6.11,
    "n_papers": 14,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes",
        "paper_id": "openreview:7Swrtm9Qsp",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?",
        "paper_id": "openreview:LlERoXEKjh",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Benign Overfitting in Out-of-Distribution Generalization of Linear Models",
        "paper_id": "openreview:6jxUsDAdAu",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "From Tempered to Benign Overfitting in ReLU Neural Networks",
        "paper_id": "openreview:LnZuxp3Tx7",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.6,
        "title": "Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension",
        "paper_id": "openreview:yjYwbZBJyl",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "nfe",
    "mean_rating": 6.1,
    "n_papers": 12,
    "p90_rating": 7.18,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Learning to Discretize Denoising Diffusion ODEs",
        "paper_id": "openreview:xDrFWUmCne",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.2,
        "title": "Bespoke Solvers for Generative Flow Models",
        "paper_id": "openreview:1PXEY7ofFX",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Linear Multistep Solver Distillation for Fast Sampling of Diffusion Models",
        "paper_id": "openreview:vkOFOUDLTn",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Improving the Training of Rectified Flows",
        "paper_id": "openreview:mSHs6C7Nfa",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Score-based Generative Models with L\u00e9vy Processes",
        "paper_id": "openreview:0Wp3VHX0Gm",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "smooth functions",
    "mean_rating": 6.1,
    "n_papers": 13,
    "p90_rating": 6.92,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes",
        "paper_id": "openreview:7Swrtm9Qsp",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits",
        "paper_id": "openreview:XCkII8nCt3",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "GloptiNets: Scalable Non-Convex Optimization with Certificates",
        "paper_id": "openreview:i28zCSsQIc",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods",
        "paper_id": "openreview:GQ1Tc3vHbt",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.25,
        "title": "Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima",
        "paper_id": "openreview:nijJN0LHqM",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "text representations",
    "mean_rating": 6.1,
    "n_papers": 14,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Interpreting CLIP's Image Representation via Text-Based Decomposition",
        "paper_id": "openreview:5Ca9sSzuDp",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation",
        "paper_id": "openreview:MLBdiWu4Fw",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Searching for High-Value Molecules Using Reinforcement Learning and Transformers",
        "paper_id": "openreview:nqlymMx42E",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Interpreting the Second-Order Effects of Neurons in CLIP",
        "paper_id": "openreview:GPDcvoFGOL",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Improved Probabilistic Image-Text Representations",
        "paper_id": "openreview:ft1mr3WlGM",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "linear bandits",
    "mean_rating": 6.1,
    "n_papers": 15,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Enhancing Preference-based Linear Bandits via Human Response Time",
        "paper_id": "openreview:aIPwlkdOut",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits",
        "paper_id": "openreview:Oeb0I3JcVc",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Fixed-Budget Differentially Private Best Arm Identification",
        "paper_id": "openreview:vrE2fqAInO",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Anytime Model Selection in Linear Bandits",
        "paper_id": "openreview:YiRX7nQ77Q",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Ensemble sampling for linear bandits: small ensembles suffice",
        "paper_id": "openreview:SO7fnIFq0o",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "excess risk",
    "mean_rating": 6.1,
    "n_papers": 13,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Feature Adaptation for Sparse Linear Regression",
        "paper_id": "openreview:aIUnoHuENG",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Private Geometric Median",
        "paper_id": "openreview:cPzjN7KABv",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods",
        "paper_id": "openreview:h7GAgbLSmC",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.4,
        "title": "On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay",
        "paper_id": "openreview:E4P5kVSKlT",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.4,
        "title": "FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression",
        "paper_id": "openreview:4L2OlXhiTM",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "sbi",
    "mean_rating": 6.09,
    "n_papers": 12,
    "p90_rating": 6.5,
    "samples": [
      {
        "avg_rating": 6.75,
        "title": "Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability",
        "paper_id": "openreview:wLiMhVJ7fx",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference",
        "paper_id": "openreview:FLFasCFJNo",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation",
        "paper_id": "openreview:ZARAiV25CW",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Flow Matching for Scalable Simulation-Based Inference",
        "paper_id": "openreview:D2cS6SoYlP",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.166666666666667,
        "title": "Robust Simulation-Based Inference under Missing Data via Neural Processes",
        "paper_id": "openreview:GsR3zRCRX5",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "markov chains",
    "mean_rating": 6.09,
    "n_papers": 15,
    "p90_rating": 7.2,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Competition Dynamics Shape Algorithmic Phases of In-Context Learning",
        "paper_id": "openreview:XgH1wfHSX8",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Deep Learning for Computing Convergence Rates of Markov Chains",
        "paper_id": "openreview:fqmSGK8C0B",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks",
        "paper_id": "openreview:BV1PHbTJzd",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.8,
        "title": "Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewards",
        "paper_id": "openreview:DqfdhM64LI",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection",
        "paper_id": "openreview:N6ba2xsmds",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "simple heuristics",
    "mean_rating": 6.08,
    "n_papers": 10,
    "p90_rating": 7.05,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models",
        "paper_id": "openreview:ZGkfoufDaU",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Aligning Human Motion Generation with Human Perceptions",
        "paper_id": "openreview:QOHgjY5KDp",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "SyllableLM: Learning Coarse Semantic Units for Speech Language Models",
        "paper_id": "openreview:dGSOn7sdWg",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.4,
        "title": "GREATS: Online Selection of High-Quality Data for LLM Training in Every Iteration",
        "paper_id": "openreview:232VcN8tSx",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.0,
        "title": "Pure Message Passing Can Estimate Common Neighbor for Link Prediction",
        "paper_id": "openreview:Xa3dVaolKo",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "ablation studies",
    "mean_rating": 6.08,
    "n_papers": 11,
    "p90_rating": 7.5,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Robustness Reprogramming for Representation Learning",
        "paper_id": "openreview:SuH5SdOXpe",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "ADIFF: Explaining audio difference using natural language",
        "paper_id": "openreview:l4fMj4Vnly",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Are Language Models Actually Useful for Time Series Forecasting?",
        "paper_id": "openreview:DV15UbHCY1",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Towards Interpreting Visual Information Processing in Vision-Language Models",
        "paper_id": "openreview:chanJGoa7f",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Transformer Fusion with Optimal Transport",
        "paper_id": "openreview:LjeqMvQpen",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "stochastic differential equation",
    "mean_rating": 6.08,
    "n_papers": 16,
    "p90_rating": 7.17,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Generative Modeling with Phase Stochastic Bridge",
        "paper_id": "openreview:tUtGjQEDd4",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations",
        "paper_id": "openreview:TYSQYx9vwd",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Conditioning non-linear and infinite-dimensional diffusion processes",
        "paper_id": "openreview:FV4an2OuFM",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "SGD vs GD: Rank Deficiency in Linear Networks",
        "paper_id": "openreview:TSaieShX3j",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Improved Sampling Algorithms for L\u00e9vy-It\u00f4 Diffusion Models",
        "paper_id": "openreview:XxCgeWSTNp",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "formal guarantees",
    "mean_rating": 6.08,
    "n_papers": 14,
    "p90_rating": 7.1,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Provable Uncertainty Decomposition via Higher-Order Calibration",
        "paper_id": "openreview:TId1SHe8JG",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Abstracting and Refining Provably Sufficient Explanations of Neural Network Predictions",
        "paper_id": "openreview:1IeCqgULIM",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Faithful Rule Extraction for Differentiable Rule Learning Models",
        "paper_id": "openreview:kBTzlxM2J1",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning",
        "paper_id": "openreview:HVFMooKrHX",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Certification of Distributional Individual Fairness",
        "paper_id": "openreview:7cnMLZvTy9",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "non - stationary environments",
    "mean_rating": 6.08,
    "n_papers": 14,
    "p90_rating": 6.62,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Online Reinforcement Learning in Non-Stationary Context-Driven Environments",
        "paper_id": "openreview:l6QnSQizmN",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Test-time Adaptation in Non-stationary Environments via Adaptive Representation Alignment",
        "paper_id": "openreview:0EfUYVMrLv",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation",
        "paper_id": "openreview:sJ88Wg5Bp5",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Rewiring Neurons in Non-Stationary Environments",
        "paper_id": "openreview:t7ozN4AXd0",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments",
        "paper_id": "openreview:yhd2kHHNtB",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "draft model",
    "mean_rating": 6.08,
    "n_papers": 10,
    "p90_rating": 6.8,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment",
        "paper_id": "openreview:mtSSFiqW6y",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Efficient Inference for Large Language Model-based Generative Recommendation",
        "paper_id": "openreview:ACSNlt77hq",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Accelerating Greedy Coordinate Gradient and General Prompt Optimization via Probe Sampling",
        "paper_id": "openreview:CMgxAaRqZh",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.0,
        "title": "Online Speculative Decoding",
        "paper_id": "openreview:Km3Kprwyua",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.0,
        "title": "DistillSpec: Improving Speculative Decoding via Knowledge Distillation",
        "paper_id": "openreview:rsY6J3ZaTF",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "3d molecules",
    "mean_rating": 6.08,
    "n_papers": 10,
    "p90_rating": 7.1,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Unified Generative Modeling of 3D Molecules with Bayesian Flow Networks",
        "paper_id": "openreview:NSVtmmzeRB",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training",
        "paper_id": "openreview:ym7pr83XQr",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Score-based 3D molecule generation with neural fields",
        "paper_id": "openreview:9lGJrkqJUw",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Training-free Multi-objective Diffusion Model for 3D Molecule Generation",
        "paper_id": "openreview:X41c4uB4k0",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Conditional Synthesis of 3D Molecules with Time Correction Sampler",
        "paper_id": "openreview:gipFTlvfF1",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "stationary point",
    "mean_rating": 6.08,
    "n_papers": 11,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "The Complexity of Two-Team Polymatrix Games with Independent Adversaries",
        "paper_id": "openreview:9VGTk2NYjF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding",
        "paper_id": "openreview:ogKE7LcvW6",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions",
        "paper_id": "openreview:x7usmidzxj",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Oracle Complexity of Single-Loop Switching Subgradient Methods for Non-Smooth Weakly Convex Functional Constrained Optimization",
        "paper_id": "openreview:A383wMho4h",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "A Restoration Network as an Implicit Prior",
        "paper_id": "openreview:x7d1qXEn1e",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "density functional theory",
    "mean_rating": 6.07,
    "n_papers": 11,
    "p90_rating": 7.33,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems",
        "paper_id": "openreview:twEvvkQqPS",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Learning local equivariant representations for quantum operators",
        "paper_id": "openreview:kpq3IIjUD3",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials",
        "paper_id": "openreview:SBCMNc3Mq3",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "A Recipe for Charge Density Prediction",
        "paper_id": "openreview:b7REKaNUTv",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Invariant subspaces and PCA in nearly matrix multiplication time",
        "paper_id": "openreview:Wyp8vsL9de",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "layer transformer",
    "mean_rating": 6.07,
    "n_papers": 11,
    "p90_rating": 7.25,
    "samples": [
      {
        "avg_rating": 8.666666666666666,
        "title": "Transformers Provably Solve Parity Efficiently with Chain of Thought",
        "paper_id": "openreview:n2NidsYDop",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Separations in the Representational Capabilities of Transformers and Recurrent Architectures",
        "paper_id": "openreview:6HUJoD3wTj",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Birth of a Transformer: A Memory Viewpoint",
        "paper_id": "openreview:3X2EbBLNsk",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Position Coupling: Improving Length Generalization of Arithmetic Transformers Using Task Structure",
        "paper_id": "openreview:5cIRdGM1uG",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.0,
        "title": "Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers",
        "paper_id": "openreview:WJ04ZX8txM",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "monte carlo",
    "mean_rating": 6.07,
    "n_papers": 14,
    "p90_rating": 7.28,
    "samples": [
      {
        "avg_rating": 8.5,
        "title": "Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems.",
        "paper_id": "openreview:nHESwXvxWK",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Quasi-Monte Carlo for 3D Sliced Wasserstein",
        "paper_id": "openreview:Wd47f7HEXg",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Langevin Quasi-Monte Carlo",
        "paper_id": "openreview:1YEF6TA8Di",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Multilevel Generative Samplers for Investigating Critical Phenomena",
        "paper_id": "openreview:YcUV5apdlq",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Fearless Stochasticity in Expectation Propagation",
        "paper_id": "openreview:3kDWoqs2X2",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "computational power",
    "mean_rating": 6.07,
    "n_papers": 10,
    "p90_rating": 7.51,
    "samples": [
      {
        "avg_rating": 7.6,
        "title": "Space and time continuous physics simulation from partial observations",
        "paper_id": "openreview:4yaFQ7181M",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "The Expressive Power of Transformers with Chain of Thought",
        "paper_id": "openreview:NjNGlPh8Wh",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Designing Skill-Compatible AI: Methodologies and Frameworks in Chess",
        "paper_id": "openreview:79rfgv3jw4",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Graph Neural Networks and Arithmetic Circuits",
        "paper_id": "openreview:0ZeONp33f0",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings",
        "paper_id": "openreview:1RrOtCmuKr",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "scale diffusion models",
    "mean_rating": 6.06,
    "n_papers": 12,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 9.0,
        "title": "Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think",
        "paper_id": "openreview:DJSZGGZYVi",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Time-Varying LoRA: Towards Effective Cross-Domain Fine-Tuning of Diffusion Models",
        "paper_id": "openreview:SgODU2mx9T",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models",
        "paper_id": "openreview:uJqKf24HGN",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Resolution Attack: Exploiting Image Compression to Deceive Deep Neural Networks",
        "paper_id": "openreview:OFukl9Qg8P",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.0,
        "title": "Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive",
        "paper_id": "openreview:EJPIzl7mgc",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "statistical guarantees",
    "mean_rating": 6.06,
    "n_papers": 12,
    "p90_rating": 6.98,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Private estimation algorithms for stochastic block models and mixture models",
        "paper_id": "openreview:Pya0kCEpDk",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models",
        "paper_id": "openreview:9WYMDgxDac",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory",
        "paper_id": "openreview:jkPDRHff3s",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence",
        "paper_id": "openreview:i5sSWKbF3b",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks",
        "paper_id": "openreview:6vnwhzRinw",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "multi - armed bandits",
    "mean_rating": 6.06,
    "n_papers": 20,
    "p90_rating": 7.03,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs",
        "paper_id": "openreview:2pNLknCTvG",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Honor Among Bandits: No-Regret Learning for Online Fair Division",
        "paper_id": "openreview:OCQbC0eDJJ",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Multi-Armed Bandits with Abstention",
        "paper_id": "openreview:U5BZcr0H7r",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "On Speeding Up Language Model Evaluation",
        "paper_id": "openreview:3cvwO5DBZn",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Locally Private and Robust Multi-Armed Bandits",
        "paper_id": "openreview:BOhnXyIPWW",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "regression problems",
    "mean_rating": 6.06,
    "n_papers": 11,
    "p90_rating": 6.8,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Better autoregressive regression with LLMs via regression-aware fine-tuning",
        "paper_id": "openreview:xGs7Ch3Vyo",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.8,
        "title": "Transformers can optimally learn regression mixture models",
        "paper_id": "openreview:sLkj91HIZU",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "New Bounds for Hyperparameter Tuning of Regression Problems Across Instances",
        "paper_id": "openreview:8QGukmdAbh",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.25,
        "title": "Curvature Enhanced Manifold Sampling",
        "paper_id": "openreview:HYWdlCPtao",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.25,
        "title": "CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions",
        "paper_id": "openreview:aINqoP32cb",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "practical algorithms",
    "mean_rating": 6.05,
    "n_papers": 12,
    "p90_rating": 6.98,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms",
        "paper_id": "openreview:Ki6DqBXss4",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "New Algorithms for the Learning-Augmented k-means Problem",
        "paper_id": "openreview:Xuyp1dGAbi",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Smoothed Online Learning for Prediction in Piecewise Affine Systems",
        "paper_id": "openreview:Izt7rDD7jN",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning",
        "paper_id": "openreview:ZITOHWeAy7",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithms",
        "paper_id": "openreview:bUgqyyNo8j",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "test error",
    "mean_rating": 6.05,
    "n_papers": 15,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning",
        "paper_id": "openreview:O0Lz8XZT2b",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees",
        "paper_id": "openreview:ed7zI29lRF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "High-dimensional Asymptotics of Denoising Autoencoders",
        "paper_id": "openreview:wbbTqsiKzl",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression",
        "paper_id": "openreview:YrTI2Zu0dd",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters",
        "paper_id": "openreview:AEglX9CHFN",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "mle",
    "mean_rating": 6.05,
    "n_papers": 18,
    "p90_rating": 7.32,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering",
        "paper_id": "openreview:6s5uXNWGIh",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Provable Offline Preference-Based Reinforcement Learning",
        "paper_id": "openreview:tVMPfEGT2w",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "How to Turn Your Knowledge Graph Embeddings into Generative Models",
        "paper_id": "openreview:RSGNGiB1q4",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "Demystifying Softmax Gating Function in Gaussian Mixture of Experts",
        "paper_id": "openreview:cto6jIIbMZ",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.75,
        "title": "Multinomial Logistic Regression: Asymptotic Normality on Null Covariates in High-Dimensions",
        "paper_id": "openreview:e1oe8F2tjV",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "model capabilities",
    "mean_rating": 6.05,
    "n_papers": 16,
    "p90_rating": 6.83,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "A Probabilistic Perspective on Unlearning and Alignment for Large Language Models",
        "paper_id": "openreview:51WraMid8K",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "DiscoveryBench: Towards Data-Driven Discovery with Large Language Models",
        "paper_id": "openreview:vyflgpwfJW",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks",
        "paper_id": "openreview:A0HKeKl4Nl",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention",
        "paper_id": "openreview:BFWdIPPLgZ",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment",
        "paper_id": "openreview:PDnEDS244P",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "broad class",
    "mean_rating": 6.05,
    "n_papers": 27,
    "p90_rating": 7.1,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Asymptotically Free Sketched Ridge Ensembles: Risks, Cross-Validation, and Tuning",
        "paper_id": "openreview:i9Vs5NGDpk",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression",
        "paper_id": "openreview:IF0Q9KY3p2",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.25,
        "title": "Improving Offline RL by Blending Heuristics",
        "paper_id": "openreview:MCl0TLboP1",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Automated Efficient Estimation using Monte Carlo Efficient Influence Functions",
        "paper_id": "openreview:2wfd3pti8v",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.8,
        "title": "Learning Hierarchical Polynomials of Multiple Nonlinear Features",
        "paper_id": "openreview:UZ893n8FXr",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "diffusion processes",
    "mean_rating": 6.04,
    "n_papers": 14,
    "p90_rating": 6.65,
    "samples": [
      {
        "avg_rating": 8.25,
        "title": "Entropic Neural Optimal Transport via Diffusion Processes",
        "paper_id": "openreview:fHyLsfMDIs",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "SyncTweedies: A General Generative Framework Based on Synchronized Diffusions",
        "paper_id": "openreview:06Vt6f2js7",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "When Graph Neural Networks Meet Dynamic Mode Decomposition",
        "paper_id": "openreview:duGygkA3QR",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Improved sampling via learned diffusions",
        "paper_id": "openreview:h4pNROsO06",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Real-World Image Variation by Aligning Diffusion Inversion Chain",
        "paper_id": "openreview:u6Ibs4hTJH",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "pac",
    "mean_rating": 6.04,
    "n_papers": 42,
    "p90_rating": 7.2,
    "samples": [
      {
        "avg_rating": 7.6,
        "title": "Optimal Learners for Realizable Regression: PAC Learning and Online Learning",
        "paper_id": "openreview:w116w62fxH",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.5,
        "title": "User-Level Differential Privacy With Few Examples Per User",
        "paper_id": "openreview:PITeSdYQkv",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.5,
        "title": "Understanding prompt engineering may not require rethinking generalization",
        "paper_id": "openreview:a745RnSFLT",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "Learning via Wasserstein-Based High Probability Generalisation Bounds",
        "paper_id": "openreview:3Wrolscjbx",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.25,
        "title": "Reliable Learning of Halfspaces under Gaussian Marginals",
        "paper_id": "openreview:0Lb8vZT1DB",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "hypothesis class",
    "mean_rating": 6.04,
    "n_papers": 11,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Human-like Few-Shot Learning via Bayesian Reasoning over Natural Language",
        "paper_id": "openreview:dVnhdm9MIg",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Active Classification with Few Queries under Misspecification",
        "paper_id": "openreview:Ma0993KZlq",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "When Is Inductive Inference Possible?",
        "paper_id": "openreview:2aGcshccuV",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Scalable Kernel Inverse Optimization",
        "paper_id": "openreview:Mktgayam7U",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Transductive Learning is Compact",
        "paper_id": "openreview:YWTpmLktMj",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "linear transformers",
    "mean_rating": 6.04,
    "n_papers": 10,
    "p90_rating": 7.03,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Parallelizing Linear Transformers with the Delta Rule over Sequence Length",
        "paper_id": "openreview:y8Rm4VNRPH",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Gated Delta Networks: Improving Mamba2 with Delta Rule",
        "paper_id": "openreview:r8H7xhYPwz",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Transformers learn to implement preconditioned gradient descent for in-context learning",
        "paper_id": "openreview:LziniAXEI9",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.6,
        "title": "A Theoretical Understanding of Self-Correction through In-context Alignment",
        "paper_id": "openreview:OtvNLTWYww",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Linear attention is (maybe) all you need (to understand Transformer optimization)",
        "paper_id": "openreview:0uI5415ry7",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "end users",
    "mean_rating": 6.03,
    "n_papers": 10,
    "p90_rating": 7.05,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Learning from End User Data with Shuffled Differential Privacy over Kernel Densities",
        "paper_id": "openreview:QjSOgxJ0hp",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks",
        "paper_id": "openreview:2rWbKbmOuM",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering",
        "paper_id": "openreview:mXpq6ut8J3",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Post-processing Private Synthetic Data for Improving Utility on Selected Measures",
        "paper_id": "openreview:neu9JlNweE",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback",
        "paper_id": "openreview:Wf2ndb8nhf",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "kernel methods",
    "mean_rating": 6.03,
    "n_papers": 20,
    "p90_rating": 6.85,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Generalization error of spectral algorithms",
        "paper_id": "openreview:3SJE1WLB4M",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Minimax optimality of convolutional neural networks for infinite dimensional input-output problems and separation from kernel methods",
        "paper_id": "openreview:EW8ZExRZkJ",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.8,
        "title": "Tanimoto Random Features for Scalable Molecular Machine Learning",
        "paper_id": "openreview:MV0INFAKGq",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.4,
        "title": "Variance-Reducing Couplings for Random Features",
        "paper_id": "openreview:oJLpXraSLb",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.25,
        "title": "Mat\u00e9rn Kernels for Tunable Implicit Surface Reconstruction",
        "paper_id": "openreview:Ox4AJ2Vurb",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "convergence analysis",
    "mean_rating": 6.03,
    "n_papers": 24,
    "p90_rating": 7.33,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Global Convergence in Neural ODEs: Impact of Activation Functions",
        "paper_id": "openreview:AoraWUmpLU",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Improved Convergence Rate for Diffusion Probabilistic Models",
        "paper_id": "openreview:SOd07Qxkw4",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Adam Exploits $\\ell_\\infty$-geometry of Loss Landscape via Coordinate-wise Adaptivity",
        "paper_id": "openreview:PUnD86UEK5",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods",
        "paper_id": "openreview:1VeQ6VBbev",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis",
        "paper_id": "openreview:pq1WUegkza",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "sde",
    "mean_rating": 6.03,
    "n_papers": 40,
    "p90_rating": 7.05,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes",
        "paper_id": "openreview:Nx4PMtJ1ER",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 8.0,
        "title": "Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time",
        "paper_id": "openreview:bTMMNT7IdW",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "Trajectory Flow Matching with Applications to Clinical Time Series Modelling",
        "paper_id": "openreview:fNakQltI1N",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "MaRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers",
        "paper_id": "openreview:yVeNBxwL5W",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Score-based Generative Modeling through Stochastic Evolution Equations in Hilbert Spaces",
        "paper_id": "openreview:GrElRvXnEj",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "discrete nature",
    "mean_rating": 6.02,
    "n_papers": 17,
    "p90_rating": 7.35,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "SymmetricDiffusers: Learning Discrete Diffusion on Finite Symmetric Groups",
        "paper_id": "openreview:EO8xpnW7aX",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Understanding prompt engineering may not require rethinking generalization",
        "paper_id": "openreview:a745RnSFLT",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "Rotating Features for Object Discovery",
        "paper_id": "openreview:fg7iyNK81W",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Poisson Variational Autoencoder",
        "paper_id": "openreview:ektPEcqGLb",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks",
        "paper_id": "openreview:uhaLuZcCjH",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "powerful approach",
    "mean_rating": 6.02,
    "n_papers": 18,
    "p90_rating": 7.3,
    "samples": [
      {
        "avg_rating": 8.75,
        "title": "Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering",
        "paper_id": "openreview:R8SolCx62K",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 8.0,
        "title": "Large Language Models to Enhance Bayesian Optimization",
        "paper_id": "openreview:OOxotBmGol",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Meta-Learning Priors Using Unrolled Proximal Networks",
        "paper_id": "openreview:b3Cu426njo",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision",
        "paper_id": "openreview:ycv2z8TYur",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "FreSh: Frequency Shifting for Accelerated Neural Representation Learning",
        "paper_id": "openreview:zMjjzXxS64",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "systematic framework",
    "mean_rating": 6.02,
    "n_papers": 12,
    "p90_rating": 7.15,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "EmbedLLM: Learning Compact Representations of Large Language Models",
        "paper_id": "openreview:Fs9EabmQrJ",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.2,
        "title": "Transport meets Variational Inference: Controlled Monte Carlo Diffusions",
        "paper_id": "openreview:PP1rudnxiW",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Diagnosing Transformers: Illuminating Feature Spaces for Clinical Decision-Making",
        "paper_id": "openreview:k581sTMyPt",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Learning semilinear neural operators: A unified recursive framework for prediction and data assimilation.",
        "paper_id": "openreview:ZMv6zKYYUs",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "Enhancing Human-AI Collaboration Through Logic-Guided Reasoning",
        "paper_id": "openreview:TWC4gLoAxY",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "matrix completion",
    "mean_rating": 6.01,
    "n_papers": 12,
    "p90_rating": 7.45,
    "samples": [
      {
        "avg_rating": 7.75,
        "title": "Implicit bias of SGD in $L_2$-regularized linear DNNs: One-way jumps from high to low rank",
        "paper_id": "openreview:P1aobHnjjj",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Neuron-Enhanced AutoEncoder Matrix Completion and Collaborative Filtering: Theory and Practice",
        "paper_id": "openreview:kPrxk6tUcg",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "A Pairwise Pseudo-likelihood Approach for Matrix Completion with Informative Missingness",
        "paper_id": "openreview:ZGN8dOhpi6",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion",
        "paper_id": "openreview:9jgODkdH0F",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Neural Harmonics: Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning",
        "paper_id": "openreview:oSYjkJKHZx",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "deep rl",
    "mean_rating": 6.01,
    "n_papers": 14,
    "p90_rating": 7.38,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning",
        "paper_id": "openreview:jXLiDKsuDo",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.4,
        "title": "Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL",
        "paper_id": "openreview:8oCrlOaYcc",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "Unlocking the Power of Representations in Long-term Novelty-based Exploration",
        "paper_id": "openreview:OwtMhMSybu",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.25,
        "title": "Bridging RL Theory and Practice with the Effective Horizon",
        "paper_id": "openreview:Lr2swAfwff",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.666666666666667,
        "title": "Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo",
        "paper_id": "openreview:nfIAEJFiBZ",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "modern machine learning models",
    "mean_rating": 6.01,
    "n_papers": 12,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Linear Mode Connectivity in Differentiable Tree Ensembles",
        "paper_id": "openreview:UqYNPyotxL",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "ReTaSA: A Nonparametric Functional Estimation Approach for Addressing Continuous Target Shift",
        "paper_id": "openreview:KdVvOA00Or",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction",
        "paper_id": "openreview:Lfy9q7Icp9",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Convergence of Distributed Adaptive Optimization with Local Updates",
        "paper_id": "openreview:VNg7srnvD9",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Scalable Bayesian Learning with posteriors",
        "paper_id": "openreview:fifXzmzeGy",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "credit assignment",
    "mean_rating": 6.01,
    "n_papers": 10,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Solving Homogeneous and Heterogeneous Cooperative Tasks with Greedy Sequential Execution",
        "paper_id": "openreview:hB2hXtxIPH",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment",
        "paper_id": "openreview:APGXBNkt6h",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "Action abstractions for amortized sampling",
        "paper_id": "openreview:ispjankYab",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Temporal-Difference Learning Using Distributed Error Signals",
        "paper_id": "openreview:8moTQjfqAV",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.285714285714286,
        "title": "Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis",
        "paper_id": "openreview:yvqqkOn9Pi",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "random initialization",
    "mean_rating": 6.01,
    "n_papers": 16,
    "p90_rating": 6.88,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "On the Parameterization of Second-Order Optimization Effective towards the Infinite Width",
        "paper_id": "openreview:g8sGBSQjYk",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "LEMON: Lossless model expansion",
        "paper_id": "openreview:3Vw7DQqq7U",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "A Unified Theory of Quantum Neural Network Loss Landscapes",
        "paper_id": "openreview:fv8TTt9srF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "DEEP NEURAL NETWORK INITIALIZATION WITH SPARSITY INDUCING ACTIVATIONS",
        "paper_id": "openreview:uvXK8Xk9Jk",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Decentralized Matrix Sensing: Statistical Guarantees and Fast Convergence",
        "paper_id": "openreview:i5sSWKbF3b",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "equivariant neural networks",
    "mean_rating": 6.0,
    "n_papers": 17,
    "p90_rating": 7.13,
    "samples": [
      {
        "avg_rating": 7.333333333333333,
        "title": "Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products",
        "paper_id": "openreview:mhyQXJ6JsK",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.333333333333333,
        "title": "On the hardness of learning under symmetries",
        "paper_id": "openreview:ARPrtuzAnQ",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Separation Power of Equivariant Neural Networks",
        "paper_id": "openreview:RAyRXQjsFl",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "A Characterization Theorem for Equivariant Networks with Point-wise Activations",
        "paper_id": "openreview:79FVDdfoSR",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.833333333333333,
        "title": "Lie Group Decompositions for Equivariant Neural Networks",
        "paper_id": "openreview:p34fRKp8qA",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "conditional generative model",
    "mean_rating": 6.0,
    "n_papers": 12,
    "p90_rating": 7.41,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Probablistic Emulation of a Global Climate Model with Spherical DYffusion",
        "paper_id": "openreview:Ib2iHIJRTh",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.5,
        "title": "Idempotence and Perceptual Image Compression",
        "paper_id": "openreview:Cy5v64DqEF",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.6,
        "title": "Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels",
        "paper_id": "openreview:LVHEcVgEGm",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities",
        "paper_id": "openreview:1Z6PSw7OL8",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "Can Generative AI Solve Your In-Context Learning Problem?  A Martingale Perspective",
        "paper_id": "openreview:bcynT7s2du",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "ridge regression",
    "mean_rating": 6.0,
    "n_papers": 10,
    "p90_rating": 6.78,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "An Effective Theory of Bias Amplification",
        "paper_id": "openreview:VoI4d6uhdr",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection",
        "paper_id": "openreview:liMSqUuVg9",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression",
        "paper_id": "openreview:YrTI2Zu0dd",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression",
        "paper_id": "openreview:BtAz4a5xDg",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.2,
        "title": "Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms",
        "paper_id": "openreview:U9e1d2xOc8",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "stochastic gradient",
    "mean_rating": 6.0,
    "n_papers": 10,
    "p90_rating": 7.1,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Distributionally Robust Optimization with Bias and Variance Reduction",
        "paper_id": "openreview:TTrzgEZt9s",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction",
        "paper_id": "openreview:9STYRIVx6u",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.8,
        "title": "Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis",
        "paper_id": "openreview:LqRGsGWOTX",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.25,
        "title": "AdaFisher: Adaptive Second Order Optimization via Fisher Information",
        "paper_id": "openreview:puTxuiK2qO",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.0,
        "title": "SOREL: A Stochastic Algorithm for Spectral Risks Minimization",
        "paper_id": "openreview:pdF86dyoS6",
        "venue": "ICLR-2025"
      }
    ]
  },
  {
    "tag": "total variation",
    "mean_rating": 6.0,
    "n_papers": 12,
    "p90_rating": 6.72,
    "samples": [
      {
        "avg_rating": 7.2,
        "title": "Computational Explorations of Total Variation Distance",
        "paper_id": "openreview:xak8c9l1nu",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian distributions",
        "paper_id": "openreview:wG12xUSqrI",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.5,
        "title": "Optimal Sample Complexity for Average Reward Markov Decision Processes",
        "paper_id": "openreview:jOm5p3q7c7",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 6.4,
        "title": "Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time",
        "paper_id": "openreview:PHbqznMa1i",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.333333333333333,
        "title": "A Unified Principle of Pessimism for Offline Reinforcement Learning under Model Mismatch",
        "paper_id": "openreview:cBY66CKEbq",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "upper bounds",
    "mean_rating": 6.0,
    "n_papers": 33,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 7.25,
        "title": "Learning via Wasserstein-Based High Probability Generalisation Bounds",
        "paper_id": "openreview:3Wrolscjbx",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics",
        "paper_id": "openreview:DZcmz9wU0i",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "Online Bayesian Persuasion Without a Clue",
        "paper_id": "openreview:XNpVZ8E1tY",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems",
        "paper_id": "openreview:n8h1z588eu",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "A Separation in Heavy-Tailed Sampling: Gaussian vs. Stable Oracles for Proximal Samplers",
        "paper_id": "openreview:zuwLGhgxtQ",
        "venue": "NeurIPS-2024"
      }
    ]
  },
  {
    "tag": "complex distributions",
    "mean_rating": 6.0,
    "n_papers": 20,
    "p90_rating": 7.23,
    "samples": [
      {
        "avg_rating": 8.0,
        "title": "Composing Unbalanced Flows for Flexible Docking and Relaxation",
        "paper_id": "openreview:gHLWTzKiZV",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.5,
        "title": "Improved Convergence Rate for Diffusion Probabilistic Models",
        "paper_id": "openreview:SOd07Qxkw4",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.2,
        "title": "Subtractive Mixture Models via Squaring: Representation and Learning",
        "paper_id": "openreview:xIHi5nxu9P",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models  via a Stochastic Integral Framework",
        "paper_id": "openreview:6awxwQEI82",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "R-divergence for Estimating Model-oriented Distribution Discrepancy",
        "paper_id": "openreview:DVWIA9v9Jm",
        "venue": "NeurIPS-2023"
      }
    ]
  },
  {
    "tag": "theoretical studies",
    "mean_rating": 6.0,
    "n_papers": 19,
    "p90_rating": 7.0,
    "samples": [
      {
        "avg_rating": 8.666666666666666,
        "title": "Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction",
        "paper_id": "openreview:TpD2aG1h0D",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Generalized Neural Collapse for a Large Number of Classes",
        "paper_id": "openreview:TmcH09s6pT",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning",
        "paper_id": "openreview:vNiI3aGcE6",
        "venue": "ICLR-2024"
      },
      {
        "avg_rating": 7.0,
        "title": "From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency",
        "paper_id": "openreview:AmEgWDhmTr",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "A Cognitive Model for Learning Abstract Relational Structures from Memory-based Decision-Making Tasks",
        "paper_id": "openreview:KC58bVmxyN",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "widespread deployment",
    "mean_rating": 6.0,
    "n_papers": 16,
    "p90_rating": 6.88,
    "samples": [
      {
        "avg_rating": 7.5,
        "title": "Controlling Language and Diffusion Models by Transporting Activations",
        "paper_id": "openreview:l2zFn6TIQi",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 7.0,
        "title": "MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention",
        "paper_id": "openreview:fPBACAbqSN",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting",
        "paper_id": "openreview:FRzCIlkM7I",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference",
        "paper_id": "openreview:vHO9mU87dc",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.5,
        "title": "Waxing-and-Waning: a Generic Similarity-based Framework for Efficient Self-Supervised Learning",
        "paper_id": "openreview:TilcG5C8bN",
        "venue": "ICLR-2024"
      }
    ]
  },
  {
    "tag": "ambient dimension",
    "mean_rating": 6.0,
    "n_papers": 11,
    "p90_rating": 6.86,
    "samples": [
      {
        "avg_rating": 7.0,
        "title": "Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics",
        "paper_id": "openreview:WHhZv8X5zF",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.857142857142857,
        "title": "Robust Feature Learning for Multi-Index Models in High Dimensions",
        "paper_id": "openreview:aKkDY1Wca0",
        "venue": "ICLR-2025"
      },
      {
        "avg_rating": 6.75,
        "title": "On Differentially Private Subspace Estimation in a Distribution-Free Setting",
        "paper_id": "openreview:aCcHVnwNlf",
        "venue": "NeurIPS-2024"
      },
      {
        "avg_rating": 6.75,
        "title": "Provable benefits of score matching",
        "paper_id": "openreview:waXoG35kbb",
        "venue": "NeurIPS-2023"
      },
      {
        "avg_rating": 6.5,
        "title": "Learning High-Degree Parities: The Crucial Role of the Initialization",
        "paper_id": "openreview:OuNIWgGGif",
        "venue": "ICLR-2025"
      }
    ]
  }
]