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

119 papers, page 1 of 3

1. NeurIPS 2026A Computational Perspective to Data Ablation Experiments
2. NeurIPS 2026A Doubly Smoothed Decentralized Stochastic Minimax Optimization Algorithm
3. NeurIPS 2026A Geometric Perspective on Reward Function Updates in Inverse Reinforcement Learning
4. NeurIPS 2026A Mechanistic Investigation of Theory of Mind in a Large Language Model
5. NeurIPS 2026A Near-optimal SQ Lower Bound for Smoothed Agnostic Learning of Boolean Halfspaces
6. NeurIPS 2026A Statistical Theory of Gated Attention through the Lens of Hierarchical Mixture of Experts
7. NeurIPS 2026A Theory of Adversary-Directed Online Learning
8. NeurIPS 2026A Theory of Spatial Continuous Attractors in Hopfield Energy Landscapes
9. NeurIPS 2026A Theory of Training Profit-Optimal LLMs
10. NeurIPS 2026A Theory on Flow Matching with Neural Networks
11. NeurIPS 2026A Unified Spectral Theory of Multimodal Losses
12. NeurIPS 2026Accelerating Neural Network Training with Augmented Koopman Dynamics
13. NeurIPS 2026Achieving Directional-Stationarity from a Single Random Direction Step
14. NeurIPS 2026Adaptively Incorporating Directional Hints into Zeroth-Order Optimization
15. NeurIPS 2026Approximate Envy-Free Allocations up to any k Goods
16. NeurIPS 2026Avoiding Obfuscation with Prover-Estimator Debate
17. NeurIPS 2026Best Arm Identification for Bandits with Shifting Means
18. NeurIPS 2026Beyond Bounded Variance: Variance-Reduced Normalized Methods for Nonconvex Optimization under Blum-Gladyshev Noise
19. NeurIPS 2026Block Optimism for Nonstationary Bandits with Latent Linear Dynamics
20. NeurIPS 2026Canonical Predictive Quotients: A Theory of Prediction under Hidden Predictive State Uncertainty with ICL Implications
21. NeurIPS 2026Characterizing Learning in Deep Neural Networks using a Tractable Algorithmic Complexity Estimator
22. NeurIPS 2026Closed-Form Last Layer Optimization
23. NeurIPS 2026Complex Schrödinger Bridges
24. NeurIPS 2026Continuous p-adic Optimization
25. NeurIPS 2026Curvature-Dependent Lower Bounds for Riemannian Online Convex Optimization
26. NeurIPS 2026Decentralized Coupled Representation Learning
27. NeurIPS 2026Deriving Hyperparameter Scaling Laws via Modern Optimization Theory
28. NeurIPS 2026Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors
29. NeurIPS 2026DisCoMBO: Steering Expert-in-the-Loop Black Box Optimization via Distributional Conformance
30. NeurIPS 2026Disentangling Optimization Geometry via Hierarchical Polar Adapters for Class-Incremental Learning
31. NeurIPS 2026ENGINE: Endogenous Variational MultiScale Optimization for Zeroth-Order LLM Fine-Tuning
32. NeurIPS 2026Estimating Implicit Regularization in Deep Learning
33. NeurIPS 2026Federated Distributionally Robust Neural Combinatorial Optimization with Convergence Guarantees
34. NeurIPS 2026Finding Simple Proofs for First-Order Optimization
35. NeurIPS 2026First-Order Regret for Online Convex Optimization with Memory and Online Nonstochastic Control
36. NeurIPS 2026Fixed Universal Transformers
37. NeurIPS 2026Follow the Regularized Leader Does Not Converge in Constrained Optimization
38. NeurIPS 2026Foundations of Categorical Equivariant Deep Learning
39. NeurIPS 2026From Cursed to Competitive: Closing the ZO–FO Gap via Input-to-State Stability
40. NeurIPS 2026From SGD to Muon: Adaptive Optimization via Schatten-p Norms
41. NeurIPS 2026Functional Gradient Descent with Adaptive Representations
42. NeurIPS 2026Generalization Without Compression Penalty: A Stability Analysis of Error Feedback
43. NeurIPS 2026Generating in the Limit with Infinitely Many Hallucinations
44. NeurIPS 2026Global Convergence in Deep Networks via the Second Law of Thermodynamics
45. NeurIPS 2026Gradient Regularized Newton Boosting Trees with Global Convergence
46. NeurIPS 2026Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes
47. NeurIPS 2026Group Distributionally Robust Optimization with Flexible Sample Queries
48. NeurIPS 2026Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
49. NeurIPS 2026High dimensional theory of two-phase optimizers
50. NeurIPS 2026High-arity Sample Compression

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.