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

463 papers, page 2 of 10

51. ICML 2026Decision-Focused Learning via Tangent-Space Projection of Prediction Error
52. ICML 2026Deep Networks Learn Deep Hierarchical Models
53. ICML 2026Distributional Alignment Games for Answer-Level Fine-Tuning
54. ICML 2026DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and Its Loss' Convexity is Dispensable)
55. ICML 2026DREAM: A Unified Framework for Drift-Corrected Federated Multi-Objective Learning
56. ICML 2026Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training
57. ICML 2026Effective Model Pruning : Measuring the Redundancy of Model Components
58. ICML 2026Enhancing LLM Training via Spectral Clipping
59. ICML 2026Equilibrium Pricing in Oligopolistic Data Markets
60. ICML 2026Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing
61. ICML 2026Exploiting weight-space symmetries for approximating curvature
62. ICML 2026Extra-Merge: Tracing the Rank-1 Subspace of Model Merging in Language Model Pre-Training
63. ICML 2026Failure-Driven Workflow Refinement
64. ICML 2026Federated Bilevel Performative Prediction
65. ICML 2026Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets
66. ICML 2026Flat Minima and Generalization: Insights from Stochastic Convex Optimization
67. ICML 2026Flatness-Aware Stochastic Gradient Langevin Dynamics
68. ICML 2026FOAM: Frequency and Operator-Error Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
69. ICML 2026From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs
70. ICML 2026Full-Batch Gradient Descent Outperforms One-Pass SGD: Sample Complexity Separation in Single-Index Learning
71. ICML 2026General Analysis of LMO-based Optimizers: Beyond Bounded Variance
72. ICML 2026Geometric Convergence of Gauss–Newton for Neural Networks: Riemannian Geometry and Adaptive Damping
73. ICML 2026Geometric Rate–Distortion Invariance for Domain Generalization
74. ICML 2026Geometry-Misalignment in Distributional Learning
75. ICML 2026Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation
76. ICML 2026Gradient Flow Sampler-based Distributionally Robust Optimization
77. ICML 2026Gradient Smoothing: Coupling Layer-wise Updates for Improved Optimization
78. ICML 2026Group Distributionally Robust Optimization-Driven RL for LLM Reasoning
79. ICML 2026Harmful Overfitting in Sobolev Spaces
80. ICML 2026Hierarchical Representations for Cross-task Automated Heuristic Design using LLMs
81. ICML 2026High-Dimensional Learning Dynamics of Quantized Models with Straight-Through Estimator
82. ICML 2026High-Probability Convergence Guarantees of Decentralized SGD
83. ICML 2026Hom-PGD$^+$: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
84. ICML 2026How Can Mamba Learn In Context with Outliers and Generalize Provably?
85. ICML 2026Hyperparameter Transfer Laws for Non-Recurrent Multi-Path Neural Networks
86. ICML 2026Improved Convergence Analysis of Topology Dependence in Decentralized SGD
87. ICML 2026Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variation
88. ICML 2026Improved Distribution Estimation in $\ell_\infty$
89. ICML 2026Improved Stochastic Optimization of LogSumExp
90. ICML 2026Improving the Robustness-Utility Trade-off in Decentralized Learning over Sparse Networks
91. ICML 2026In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
92. ICML 2026Inconsistency-Aware Minimization: Improving Generalization with Unlabeled Data
93. ICML 2026Is Spurious Correlation Removal Always Learnable?
94. ICML 2026Iterative Robust Satisficing: Minimizing Performance Degradation Under Distribution Shift
95. ICML 2026ITSPACE: Monotone Gaussian Optimal Transport Updates
96. ICML 2026Joint-Space Empowerment as a Theory of Dexterous Motor Coordination
97. ICML 2026L-SR1: Learned Symmetric-Rank-One Preconditioning
98. ICML 2026Last-iterate Convergence of ADMM on Multi-affine Quadratic Equality Constrained Problem
99. ICML 2026Learn to change the world: Multi-level reinforcement learning with model-changing actions
100. ICML 2026Learning Credal Ensembles via Distributionally Robust 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.