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

393 papers, page 7 of 8

301. NeurIPS 2024Online Weighted Paging with Unknown Weights
302. NeurIPS 2024Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift
303. NeurIPS 2024Optimal Algorithms for Augmented Testing of Discrete Distributions
304. NeurIPS 2024Optimal Algorithms for Learning Partitions with Faulty Oracles
305. NeurIPS 2024Optimal deep learning of holomorphic operators between Banach spaces
306. NeurIPS 2024Optimal Hypothesis Selection in (Almost) Linear Time
307. NeurIPS 2024Optimal Parallelization of Boosting
308. NeurIPS 2024Optimal Private and Communication Constraint Distributed Goodness-of-Fit Testing for Discrete Distributions in the Large Sample Regime
309. NeurIPS 2024Optimal Rates for Vector-Valued Spectral Regularization Learning Algorithms
310. NeurIPS 2024Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality
311. NeurIPS 2024PAC-Bayes-Chernoff bounds for unbounded losses
312. NeurIPS 2024Precise asymptotics of reweighted least-squares algorithms for linear diagonal networks
313. NeurIPS 2024Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
314. NeurIPS 2024Prospective Learning: Learning for a Dynamic Future
315. NeurIPS 2024Provable Benefit of Cutout and CutMix for Feature Learning
316. NeurIPS 2024Provable Benefits of Complex Parameterizations for Structured State Space Models
317. NeurIPS 2024Provable Tempered Overfitting of Minimal Nets and Typical Nets
318. NeurIPS 2024Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
319. NeurIPS 2024Quantifying the Gain in Weak-to-Strong Generalization
320. NeurIPS 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
321. NeurIPS 2024Randomized Truthful Auctions with Learning Agents
322. NeurIPS 2024Realizable $H$-Consistent and Bayes-Consistent Loss Functions for Learning to Defer
323. NeurIPS 2024Reciprocal Learning
324. NeurIPS 2024Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
325. NeurIPS 2024Reliable Learning of Halfspaces under Gaussian Marginals
326. NeurIPS 2024Replicability in Learning: Geometric Partitions and KKM-Sperner Lemma
327. NeurIPS 2024Replicable Uniformity Testing
328. NeurIPS 2024Rethinking 3D Convolution in $\ell_p$-norm Space
329. NeurIPS 2024Robust Mixture Learning when Outliers Overwhelm Small Groups
330. NeurIPS 2024Robust Sparse Regression with Non-Isotropic Designs
331. NeurIPS 2024Sample and Computationally Efficient Robust Learning of Gaussian Single-Index Models
332. NeurIPS 2024Sample-Efficient Agnostic Boosting
333. NeurIPS 2024Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models
334. NeurIPS 2024Scaling laws for learning with real and surrogate data
335. NeurIPS 2024Scaling Laws in Linear Regression: Compute, Parameters, and Data
336. NeurIPS 2024Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting
337. NeurIPS 2024Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
338. NeurIPS 2024Separation and Bias of Deep Equilibrium Models on Expressivity and Learning Dynamics
339. NeurIPS 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
340. NeurIPS 2024Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
341. NeurIPS 2024Soft ascent-descent as a stable and flexible alternative to flooding
342. NeurIPS 2024Solving Sparse \& High-Dimensional-Output Regression via Compression
343. NeurIPS 2024Stability and Generalization of Asynchronous SGD: Sharper Bounds Beyond Lipschitz and Smoothness
344. NeurIPS 2024Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm
345. NeurIPS 2024Statistical Multicriteria Benchmarking via the GSD-Front
346. NeurIPS 2024Statistical-Computational Trade-offs for Density Estimation
347. NeurIPS 2024Stratified Prediction-Powered Inference for Effective Hybrid Evaluation of Language Models
348. NeurIPS 2024Supervised Kernel Thinning
349. NeurIPS 2024Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
350. NeurIPS 2024Symmetric Linear Bandits with Hidden Symmetry

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.