Theory · learning theory papers, 2023–2026

Key findings

• Theory · learning theory went from 86 papers in 2023 to 482 in 2026, roughly 5.6x in three years.
• NeurIPS publishes the most Theory · learning theory work: 540 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with Social aspects · accountability transparency and interpretability: papers tagged with both rose from 0 in 2023 to 33 in 2026.
• Among the 5 topics under Theory and Statistics, it ranks #2 by 2026 paper count; the fastest grower in the group, Theory · optimization, is up 4.7x versus 5.6x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Theory · learning theory papers

1,350 papers, page 27 of 27

1301. NeurIPS 2023Bandit Social Learning under Myopic Behavior
1302. NeurIPS 2023Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space
1303. NeurIPS 2023Bridging RL Theory and Practice with the Effective Horizon
1304. NeurIPS 2023Characterization of Overfitting in Robust Multiclass Classification
1305. NeurIPS 2023Common Ground in Cooperative Communication
1306. NeurIPS 2023Delegated Classification
1307. NeurIPS 2023Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
1308. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
1309. NeurIPS 2023Estimating the Rate-Distortion Function by Wasserstein Gradient Descent
1310. NeurIPS 2023Faster Relative Entropy Coding with Greedy Rejection Coding
1311. NeurIPS 2023Feature Selection in the Contrastive Analysis Setting
1312. NeurIPS 2023Finding Safe Zones of Markov Decision Processes Policies
1313. NeurIPS 2023Generalized Belief Transport
1314. NeurIPS 2023Information Theoretic Lower Bounds for Information Theoretic Upper Bounds
1315. NeurIPS 2023Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
1316. NeurIPS 2023Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning
1317. NeurIPS 2023Max-Sliced Mutual Information
1318. NeurIPS 2023Nash Regret Guarantees for Linear Bandits
1319. NeurIPS 2023Near-Linear Time Algorithm for the Chamfer Distance
1320. NeurIPS 2023New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
1321. NeurIPS 2023No-regret Algorithms for Fair Resource Allocation
1322. NeurIPS 2023On Generalization Bounds for Projective Clustering
1323. NeurIPS 2023On kernel-based statistical learning theory in the mean field limit
1324. NeurIPS 2023On Slicing Optimality for Mutual Information
1325. NeurIPS 2023On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm
1326. NeurIPS 2023Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games
1327. NeurIPS 2023Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation
1328. NeurIPS 2023Provable benefits of score matching
1329. NeurIPS 2023Provably Safe Reinforcement Learning with Step-wise Violation Constraints
1330. NeurIPS 2023Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework
1331. NeurIPS 2023Reliable learning in challenging environments
1332. NeurIPS 2023Replicability in Reinforcement Learning
1333. NeurIPS 2023Replicable Clustering
1334. NeurIPS 2023Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
1335. NeurIPS 2023Sharp Recovery Thresholds of Tensor PCA Spectral Algorithms
1336. NeurIPS 2023Sharp Spectral Rates for Koopman Operator Learning
1337. NeurIPS 2023Similarity-based cooperative equilibrium
1338. NeurIPS 2023Smoothed Analysis of Sequential Probability Assignment
1339. NeurIPS 2023Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
1340. NeurIPS 2023Strategic Data Sharing between Competitors
1341. NeurIPS 2023Structured Prediction with Stronger Consistency Guarantees
1342. NeurIPS 2023Temporally Disentangled Representation Learning under Unknown Nonstationarity
1343. NeurIPS 2023The Crucial Role of Normalization in Sharpness-Aware Minimization
1344. NeurIPS 2023The noise level in linear regression with dependent data
1345. NeurIPS 2023Towards Characterizing the First-order Query Complexity of Learning (Approximate) Nash Equilibria in Zero-sum Matrix Games
1346. NeurIPS 2023Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
1347. NeurIPS 2023Two-Stage Learning to Defer with Multiple Experts
1348. NeurIPS 2023What can a Single Attention Layer Learn? A Study Through the Random Features Lens
1349. NeurIPS 2023When are ensembles really effective?
1350. NeurIPS 2023Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria

FAQ

How many Theory · learning theory papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,350 in total: 86 in 2023, 393 in 2024, 389 in 2025, 482 in 2026.

How many Theory · learning theory papers are at NeurIPS 2026?

NeurIPS 2026 has 112 Theory · learning theory papers, versus 106 the year before.

How many Theory · learning theory papers are at ICLR 2026?

ICLR 2026 has 200 Theory · learning theory papers, versus 147 the year before.

How many Theory · learning theory papers are at ICML 2026?

ICML 2026 has 170 Theory · learning theory papers, versus 132 the year before.

How many Theory · learning theory papers are at CVPR 2025?

CVPR 2025 has 4 Theory · learning theory papers, versus 1 the year before.

Which conference publishes the most Theory · learning theory papers?

NeurIPS, with 540 papers from 2023 to 2026.