Theory · optimization papers, 2023–2026

Key findings

• Theory · optimization went from 123 papers in 2023 to 584 in 2026, roughly 4.7x in three years.
• NeurIPS publishes the most Theory · optimization work: 778 papers from 2023 to 2026, 42% of the four-venue total.
• The fastest-growing overlap is with Low-Rank Adaptation: papers tagged with both rose from 0 in 2023 to 31 in 2026.
• Among the 5 topics under Theory and Statistics, it ranks #1 by 2026 paper count; the fastest grower in the group, Theory · learning theory, is up 5.6x versus 4.7x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Mechanistic Investigation of Theory of Mind in a Large Language Model
• ICML 2026A Direct Second-Order Method for Solving Two-Player Zero-Sum Games
• ICLR 2026A Convergence Analysis of Adaptive Optimizers under Floating-point Quantization
• CVPR 2026TriSim Tri-Dimensional Similarity Modeling with Extreme Value Theory for False-Negative
• NeurIPS 20253BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs
• ICML 2025Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
• ICLR 2025A Coefficient Makes SVRG Effective
• CVPR 2025Dynamic Stereotype Theory Induced Micro-expression Recognition with Oriented Deformation
• NeurIPS 2024A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem
• ICML 2024A Statistical Theory of Regularization-Based Continual Learning
• ICLR 2024A Discretization Framework for Robust Contextual Stochastic Optimization
• NeurIPS 2023[Re] Bandit Theory and Thompson Sampling-guided Directed Evolution for Sequence Optimization
• ICML 2023Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity
• ICLR 2023Adaptive Optimization in the $\infty$-Width Limit
• CVPR 2023Deep Hashing With Minimal-Distance-Separated Hash Centers
• NeurIPS 2026Avoiding Obfuscation with Prover-Estimator Debate
• ICML 2026A General Framework for Dynamic Consistent Submodular Maximization
• ICLR 2026A Memory-Efficient Hierarchical Algorithm for Large-scale Optimal Transport Problems
• CVPR 2026FedAdamom Adaptive Momentum for Improved Generalization in Federated Optimization
• NeurIPS 2025A Beyond-Worst-Case Analysis of Greedy k-means++

All Theory · optimization papers

67 papers, page 1 of 2

1. ICML 2024A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
2. ICML 2024A sampling theory perspective on activations for implicit neural representations
3. ICML 2024A Statistical Theory of Regularization-Based Continual Learning
4. ICML 2024A Theory of Fault-Tolerant Learning
5. ICML 2024A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
6. ICML 2024ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints
7. ICML 2024Advancing Dynamic Sparse Training by Exploring Optimization Opportunities
8. ICML 2024Autonomous Sparse Mean-CVaR Portfolio Optimization
9. ICML 2024Barrier Algorithms for Constrained Non-Convex Optimization
10. ICML 2024Bayesian Optimization of Function Networks with Partial Evaluations
11. ICML 2024Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints
12. ICML 2024Decentralized Convex Finite-Sum Optimization with Better Dependence on Condition Numbers
13. ICML 2024Delving into the Convergence of Generalized Smooth Minimax Optimization
14. ICML 2024Differentiability and Optimization of Multiparameter Persistent Homology
15. ICML 2024DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation
16. ICML 2024Does Label Smoothing Help Deep Partial Label Learning?
17. ICML 2024Double Momentum Method for Lower-Level Constrained Bilevel Optimization
18. ICML 2024Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient
19. ICML 2024Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization
20. ICML 2024Federated Optimization with Doubly Regularized Drift Correction
21. ICML 2024Fundamental Benefit of Alternating Updates in Minimax Optimization
22. ICML 2024Generalized Preference Optimization: A Unified Approach to Offline Alignment
23. ICML 2024High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
24. ICML 2024How Free is Parameter-Free Stochastic Optimization?
25. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
26. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
27. ICML 2024Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
28. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
29. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
30. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
31. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
32. ICML 2024Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
33. ICML 2024Model Alignment as Prospect Theoretic Optimization
34. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
35. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
36. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
37. ICML 2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
38. ICML 2024On Statistical Learning Theory for Distributional Inputs
39. ICML 2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
40. ICML 2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
41. ICML 2024On the Last-Iterate Convergence of Shuffling Gradient Methods
42. ICML 2024Open Ad Hoc Teamwork with Cooperative Game Theory
43. ICML 2024Optimization without Retraction on the Random Generalized Stiefel Manifold
44. ICML 2024PIDformer: Transformer Meets Control Theory
45. ICML 2024Plug-in Performative Optimization
46. ICML 2024Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
47. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
48. ICML 2024Position: Optimization in SciML Should Employ the Function Space Geometry
49. ICML 2024Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
50. ICML 2024Principled Preferential Bayesian Optimization

FAQ

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

1,871 in total: 123 in 2023, 494 in 2024, 670 in 2025, 584 in 2026.

How many Theory · optimization papers are at NeurIPS 2026?

NeurIPS 2026 has 119 Theory · optimization papers, versus 318 the year before.

How many Theory · optimization papers are at ICLR 2026?

ICLR 2026 has 244 Theory · optimization papers, versus 196 the year before.

How many Theory · optimization papers are at ICML 2026?

ICML 2026 has 214 Theory · optimization papers, versus 147 the year before.

How many Theory · optimization papers are at CVPR 2026?

CVPR 2026 has 7 Theory · optimization papers, versus 9 the year before.

Which conference publishes the most Theory · optimization papers?

NeurIPS, with 778 papers from 2023 to 2026.