理论 · learning theory论文趋势 2023–2026

学习理论及相关：learning_theory

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,350 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

理论 · learning theory论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
202357245086
2024265106211393
20251061471324389
20261122001700482

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

全部理论 · learning theory论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 540 篇，第 7 / 11 页

  1. NeurIPS 2024Generalized Eigenvalue Problems with Generative Priors
  2. NeurIPS 2024Generalized Tensor Decomposition for Understanding Multi-Output Regression under Combinatorial Shifts
  3. NeurIPS 2024Generative Forests
  4. NeurIPS 2024Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning
  5. NeurIPS 2024Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
  6. NeurIPS 2024Group-wise oracle-efficient algorithms for online multi-group learning
  7. NeurIPS 2024Hardness of Learning Neural Networks under the Manifold Hypothesis
  8. NeurIPS 2024Harnessing Multiple Correlated Networks for Exact Community Recovery
  9. NeurIPS 2024High-dimensional (Group) Adversarial Training in Linear Regression
  10. NeurIPS 2024How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
  11. NeurIPS 2024How Far Can Transformers Reason? The Globality Barrier and Inductive Scratchpad
  12. NeurIPS 2024How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
  13. NeurIPS 2024How many classifiers do we need?
  14. NeurIPS 2024How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective
  15. NeurIPS 2024How to Boost Any Loss Function
  16. NeurIPS 2024How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression
  17. NeurIPS 2024Hyperbolic Embeddings of Supervised Models
  18. NeurIPS 2024Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees
  19. NeurIPS 2024Implicit Regularization Paths of Weighted Neural Representations
  20. NeurIPS 2024Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
  21. NeurIPS 2024Improved Sample Complexity Bounds for Diffusion Model Training
  22. NeurIPS 2024Improved Sample Complexity for Multiclass PAC Learning
  23. NeurIPS 2024Improving Adaptivity via Over-Parameterization in Sequence Models
  24. NeurIPS 2024Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
  25. NeurIPS 2024In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
  26. NeurIPS 2024In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
  27. NeurIPS 2024Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
  28. NeurIPS 2024Information-theoretic Generalization Analysis for Expected Calibration Error
  29. NeurIPS 2024Information-theoretic Limits of Online Classification with Noisy Labels
  30. NeurIPS 2024Invariant subspaces and PCA in nearly matrix multiplication time
  31. NeurIPS 2024Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions
  32. NeurIPS 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
  33. NeurIPS 2024Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
  34. NeurIPS 2024Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL
  35. NeurIPS 2024Label Noise: Ignorance Is Bliss
  36. NeurIPS 2024Language Generation in the Limit
  37. NeurIPS 2024Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
  38. NeurIPS 2024Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
  39. NeurIPS 2024Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks
  40. NeurIPS 2024Learning from Noisy Labels via Conditional Distributionally Robust Optimization
  41. NeurIPS 2024Learning from Snapshots of Discrete and Continuous Data Streams
  42. NeurIPS 2024Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
  43. NeurIPS 2024Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
  44. NeurIPS 2024Learning Partitions from Context
  45. NeurIPS 2024Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
  46. NeurIPS 2024Learning the Infinitesimal Generator of Stochastic Diffusion Processes
  47. NeurIPS 2024Learning via Surrogate PAC-Bayes
  48. NeurIPS 2024Learning-Augmented Algorithms with Explicit Predictors
  49. NeurIPS 2024Least Squares Regression Can Exhibit Under-Parameterized Double Descent
  50. NeurIPS 2024Length Optimization in Conformal Prediction

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用 paperlist 查理论 · learning theory在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

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常见问题

2023–2026 年四大会议一共有多少篇理论 · learning theory论文？
共 1,350 篇，按年份分别是 2023 年 86 篇、2024 年 393 篇、2025 年 389 篇、2026 年 482 篇。
NeurIPS 2026 有多少篇理论 · learning theory论文？
NeurIPS 2026 有 112 篇理论 · learning theory论文，上一届是 106 篇。
ICLR 2026 有多少篇理论 · learning theory论文？
ICLR 2026 有 200 篇理论 · learning theory论文，上一届是 147 篇。
ICML 2026 有多少篇理论 · learning theory论文？
ICML 2026 有 170 篇理论 · learning theory论文，上一届是 132 篇。
CVPR 2025 有多少篇理论 · learning theory论文？
CVPR 2025 有 4 篇理论 · learning theory论文，上一届是 1 篇。
哪个会议的理论 · learning theory论文最多？
NeurIPS，2023–2026 年共 540 篇。

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