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

304 papers, page 2 of 7

51. NeurIPS 2024Contracting with a Learning Agent
52. NeurIPS 2024Controlling Continuous Relaxation for Combinatorial Optimization
53. NeurIPS 2024Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
54. NeurIPS 2024Convergence Analysis of Split Federated Learning on Heterogeneous Data
55. NeurIPS 2024Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods
56. NeurIPS 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
57. NeurIPS 2024Credal Learning Theory
58. NeurIPS 2024CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
59. NeurIPS 2024Decision-Focused Learning with Directional Gradients
60. NeurIPS 2024Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
61. NeurIPS 2024Deep linear networks for regression are implicitly regularized towards flat minima
62. NeurIPS 2024Derivatives of Stochastic Gradient Descent in parametric optimization
63. NeurIPS 2024Diffeomorphic interpolation for efficient persistence-based topological optimization
64. NeurIPS 2024Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandits
65. NeurIPS 2024Directional Smoothness and Gradient Methods: Convergence and Adaptivity
66. NeurIPS 2024Discretely beyond $1/e$: Guided Combinatorial Algortihms for Submodular Maximization
67. NeurIPS 2024Disentangling Linear Quadratic Control with Untrusted ML Predictions
68. NeurIPS 2024Distribution Learning with Valid Outputs Beyond the Worst-Case
69. NeurIPS 2024Distributional Preference Alignment of LLMs via Optimal Transport
70. NeurIPS 2024DistrictNet: Decision-aware learning for geographical districting
71. NeurIPS 2024Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
72. NeurIPS 2024Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD
73. NeurIPS 2024Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences
74. NeurIPS 2024Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
75. NeurIPS 2024Dual Lagrangian Learning for Conic Optimization
76. NeurIPS 2024Efficiency of the First-Price Auction in the Autobidding World
77. NeurIPS 2024Efficient Combinatorial Optimization via Heat Diffusion
78. NeurIPS 2024Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability
79. NeurIPS 2024Efficient Leverage Score Sampling for Tensor Train Decomposition
80. NeurIPS 2024Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
81. NeurIPS 2024Emergence of heavy tails in homogenized stochastic gradient descent
82. NeurIPS 2024Euclidean distance compression via deep random features
83. NeurIPS 2024Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization
84. NeurIPS 2024Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization
85. NeurIPS 2024Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
86. NeurIPS 2024Fair Secretaries with Unfair Predictions
87. NeurIPS 2024Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning
88. NeurIPS 2024Faster Accelerated First-order Methods for Convex Optimization with Strongly Convex Function Constraints
89. NeurIPS 2024Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
90. NeurIPS 2024Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
91. NeurIPS 2024FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
92. NeurIPS 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction
93. NeurIPS 2024First-Order Methods for Linearly Constrained Bilevel Optimization
94. NeurIPS 2024First-Order Minimax Bilevel Optimization
95. NeurIPS 2024Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations
96. NeurIPS 2024From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimization
97. NeurIPS 2024FUGAL: Feature-fortified Unrestricted Graph Alignment
98. NeurIPS 2024Functional Bilevel Optimization for Machine Learning
99. NeurIPS 2024Functionally Constrained Algorithm Solves Convex Simple Bilevel Problem
100. NeurIPS 2024Fundamental Convergence Analysis of Sharpness-Aware Minimization

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.