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
- 理论 · learning theory论文从 2023 年的 86 篇增长到 2026 年的 482 篇,三年间扩大到约 5.6 倍。
- NeurIPS 是发理论 · learning theory论文最多的会议,2023–2026 年共 540 篇,占四个会议总量的 40%。
- 交叉方向里涨得最快的是社会层面 · accountability transparency and interpretability:同时属于两个主题的论文从 2023 年的 0 篇增至 2026 年的 33 篇。
- 在「理论与统计」的 5 个主题里,它的 2026 年论文数排第 2;同组增长最快的理论 · optimization是 4.7 倍,理论 · learning theory是 5.6 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 57 | 24 | 5 | 0 | 86 |
| 2024 | 265 | 106 | 21 | 1 | 393 |
| 2025 | 106 | 147 | 132 | 4 | 389 |
| 2026 | 112 | 200 | 170 | 0 | 482 |
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
- NeurIPS 2026A dynamical systems theory of reward-modulated learning in linear recurrent networks
- ICML 2026A Fine-Grained Understanding of Uniform Convergence for Halfspaces
- ICLR 2026A Law of Data Reconstruction for Random Features (And Beyond)
- NeurIPS 2025A Geometric Analysis of PCA
- ICML 2025A Generalization Theory for Zero-Shot Prediction
- ICLR 2025A Solvable Attention for Neural Scaling Laws
- CVPR 2025Dynamic Stereotype Theory Induced Micro-expression Recognition with Oriented Deformation
- NeurIPS 20244+3 Phases of Compute-Optimal Neural Scaling Laws
- ICML 2024A Statistical Theory of Regularization-Based Continual Learning
- ICLR 2024A Sublinear Adversarial Training Algorithm
- CVPR 2024Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory
- NeurIPS 2023$\varepsilon$-fractional core stability in Hedonic Games.
- ICML 2023Robust Collaborative Learning with Linear Gradient Overhead
- ICLR 2023Bispectral Neural Networks
- NeurIPS 2026A Mechanistic Investigation of Theory of Mind in a Large Language Model
- ICML 2026A Random Matrix Theory of Masked Self-Supervised Learning
- ICLR 2026A New Approach to Controlling Linear Dynamical Systems
- NeurIPS 2025Approximation theory for 1-Lipschitz ResNets
- ICML 2025A Two-Stage Learning-to-Defer Approach for Multi-Task Learning
- ICLR 2025A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
全部理论 · learning theory论文
共 1,350 篇,第 23 / 27 页
- NeurIPS 2024Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
- NeurIPS 2024Improved Sample Complexity Bounds for Diffusion Model Training
- NeurIPS 2024Improved Sample Complexity for Multiclass PAC Learning
- NeurIPS 2024Improving Adaptivity via Over-Parameterization in Sequence Models
- NeurIPS 2024Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
- NeurIPS 2024In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
- NeurIPS 2024In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
- NeurIPS 2024Inductive biases of multi-task learning and finetuning: multiple regimes of feature reuse
- NeurIPS 2024Information-theoretic Generalization Analysis for Expected Calibration Error
- NeurIPS 2024Information-theoretic Limits of Online Classification with Noisy Labels
- NeurIPS 2024Invariant subspaces and PCA in nearly matrix multiplication time
- NeurIPS 2024Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions
- NeurIPS 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
- NeurIPS 2024Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
- NeurIPS 2024Is O(log N) practical? Near-Equivalence Between Delay Robustness and Bounded Regret in Bandits and RL
- NeurIPS 2024Label Noise: Ignorance Is Bliss
- NeurIPS 2024Language Generation in the Limit
- NeurIPS 2024Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
- NeurIPS 2024Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
- NeurIPS 2024Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks
- NeurIPS 2024Learning from Noisy Labels via Conditional Distributionally Robust Optimization
- NeurIPS 2024Learning from Snapshots of Discrete and Continuous Data Streams
- NeurIPS 2024Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
- NeurIPS 2024Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
- NeurIPS 2024Learning Partitions from Context
- NeurIPS 2024Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
- NeurIPS 2024Learning the Infinitesimal Generator of Stochastic Diffusion Processes
- NeurIPS 2024Learning via Surrogate PAC-Bayes
- NeurIPS 2024Learning-Augmented Algorithms with Explicit Predictors
- NeurIPS 2024Least Squares Regression Can Exhibit Under-Parameterized Double Descent
- NeurIPS 2024Length Optimization in Conformal Prediction
- NeurIPS 2024Linear Regression using Heterogeneous Data Batches
- NeurIPS 2024Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter Optimization
- NeurIPS 2024Masked Hard-Attention Transformers Recognize Exactly the Star-Free Languages
- NeurIPS 2024Matrix Denoising with Doubly Heteroscedastic Noise: Fundamental Limits and Optimal Spectral Methods
- NeurIPS 2024Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
- NeurIPS 2024Metric Transforms and Low Rank Representations of Kernels for Fast Attention
- NeurIPS 2024Mission Impossible: A Statistical Perspective on Jailbreaking LLMs
- NeurIPS 2024Mixed Dynamics In Linear Networks: Unifying the Lazy and Active Regimes
- NeurIPS 2024Model Collapse Demystified: The Case of Regression
- NeurIPS 2024Multi-Label Learning with Stronger Consistency Guarantees
- NeurIPS 2024Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
- NeurIPS 2024Nature-Inspired Local Propagation
- NeurIPS 2024Near-Optimality of Contrastive Divergence Algorithms
- NeurIPS 2024Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
- NeurIPS 2024Neural Collapse To Multiple Centers For Imbalanced Data
- NeurIPS 2024Neural Conditional Probability for Uncertainty Quantification
- NeurIPS 2024Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
- NeurIPS 2024NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory
- NeurIPS 2024Noisy Label Learning with Instance-Dependent Outliers: Identifiability via Crowd Wisdom
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后,写综述、挑方向或准备投稿时,不用离开对话框就能拿到这页的数字:每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文,AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查理论 · learning theory在 NeurIPS 2023–2026 的论文趋势,哪一年涨得最快?
AI 会调用的工具:get_topic_stats {"topic": "theory-learning-theory"}
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。