理论 · learning theory论文趋势 2023–2026

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。

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 328 篇，第 3 / 7 页

101. ICML 2026On the Accuracy of Newton Step and Influence Function Data Attributions
102. ICML 2026On the Emergence of Implicit Curriculum in RLVR Learning Dynamics
103. ICML 2026On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification
104. ICML 2026On the Power of Source Screening for Learning Shared Feature Extractors
105. ICML 2026On the Sharp Input-Output Analysis of Nonlinear Systems under Adversarial Attacks
106. ICML 2026On the Theory of Continual Learning with Gradient Descent for Neural Networks
107. ICML 2026On the Variability of Concept Activation Vectors
108. ICML 2026Online Learning with Recency: Algorithms for Sliding-window Streaming Multi-armed Bandits
109. ICML 2026Optimal Learning from Label Proportions with General Loss Functions
110. ICML 2026Optimal Stopping in Latent Diffusion Models
111. ICML 2026Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
112. ICML 2026PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
113. ICML 2026PCA of Probability Measures: Sparse and Dense Sampling Regimes
114. ICML 2026Performative Learning Theory
115. ICML 2026Positive Distribution Shift as a Framework for Understanding Tractable Learning
116. ICML 2026Provable Bounds for the Learnability of Sample-Compressible Families from Noisy Samples
117. ICML 2026Provable Sample Efficiency of Curriculum Post-Training for Transformer Reasoning
118. ICML 2026Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
119. ICML 2026Provably Label-Efficient Conformal Prediction
120. ICML 2026Provably Learning Attention with Queries
121. ICML 2026Rational Neural Networks have Expressivity Advantages
122. ICML 2026Rational Transductors
123. ICML 2026Realizable Bayes-Consistency for General Metric Losses
124. ICML 2026Refined Analysis of Entropy-Regularized Actor-Critic
125. ICML 2026Representation Learning for Equivariant Inference with Guarantees
126. ICML 2026Revisiting the Volume Hypothesis
127. ICML 2026Sample Complexity Bounds for Robust Mean Estimation with Mean-Shift Contamination
128. ICML 2026SAQNN: Spectral Adaptive Quantum Neural Network as a Universal Approximator
129. ICML 2026Scalable and Stable Estimation of Amari $\alpha$-Divergence using Random Fourier Features
130. ICML 2026Scaling Laws for Precision in High-Dimensional Linear Regression
131. ICML 2026Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees
132. ICML 2026Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
133. ICML 2026Sharp Empirical Bernstein Inequalities for the Variance of Bounded Random Variables
134. ICML 2026Sharp Inequalities between Total Variation and Hellinger Distances for Gaussian Mixtures
135. ICML 2026Sharper Generalization Guarantees for Asynchronous SGD: Beyond Lipschitzness, Smoothness and Data Homogeneity
136. ICML 2026Sparse Regression with $\ell_0$ Constraints for $\alpha$-Mixing Time Series: Algorithms and Guarantees
137. ICML 2026Stability and Generalization of Nonconvex Optimization with Heavy-Tailed Noise
138. ICML 2026Stability beyond Bounded Differences: Sharp Generalization Bounds under Finite $L_p$ Moments
139. ICML 2026Statistical Consistency and Generalization of Contrastive Representation Learning
140. ICML 2026Statistically Undetectable Backdoors in Deep Neural Networks
141. ICML 2026The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions, and How Hartigan's Algorithm Avoids It
142. ICML 2026The Data Manifold under the Microscope
143. ICML 2026The Devil is in the Condition Numbers: Why is GLU Better than non-GLU Structure?
144. ICML 2026The Entropic Signature of Class Speciation in Diffusion Models
145. ICML 2026The Fisher Dimension: Instance-Dependent Complexity for Causal Discovery
146. ICML 2026The Implicit Bias of Steepest Descent with Mini-batch Stochastic Gradient
147. ICML 2026The Interplay Between Interpolation and Aggregation in Regression: Optimal Sample Complexity
148. ICML 2026The Optimal Sample Complexity of Linear Contracts
149. ICML 2026The Stability of Singular Distribution: A Spectral Perspective on the Two-Phase Dynamics of Language Model Pre-training
150. ICML 2026Theoretical Investigation on Inductive Bias of Isolation Forest

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。