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 3 of 7

101. NeurIPS 2024Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models
102. NeurIPS 2024Generalized Fast Exact Conformalization
103. NeurIPS 2024Generic Unsupervised Optimization for a Latent Variable Model With Exponential Family Observables
104. NeurIPS 2024GLinSAT: The General Linear Satisfiability Neural Network Layer By Accelerated Gradient Descent
105. NeurIPS 2024Gradient Guidance for Diffusion Models: An Optimization Perspective
106. NeurIPS 2024Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization
107. NeurIPS 2024Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
108. NeurIPS 2024Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
109. NeurIPS 2024Hardness of Learning Neural Networks under the Manifold Hypothesis
110. NeurIPS 2024High-probability complexity bounds for stochastic non-convex minimax optimization
111. NeurIPS 2024How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
112. NeurIPS 2024How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks
113. NeurIPS 2024How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective
114. NeurIPS 2024How to Boost Any Loss Function
115. NeurIPS 2024How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression
116. NeurIPS 2024HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links
117. NeurIPS 2024Implicit Bias of Mirror Flow on Separable Data
118. NeurIPS 2024Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
119. NeurIPS 2024Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
120. NeurIPS 2024Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces
121. NeurIPS 2024Improved Particle Approximation Error for Mean Field Neural Networks
122. NeurIPS 2024Improved Sample Complexity for Multiclass PAC Learning
123. NeurIPS 2024Improving Equivariant Model Training via Constraint Relaxation
124. NeurIPS 2024Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
125. NeurIPS 2024In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
126. NeurIPS 2024Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques
127. NeurIPS 2024Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence
128. NeurIPS 2024Infinite Limits of Multi-head Transformer Dynamics
129. NeurIPS 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
130. NeurIPS 2024IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs
131. NeurIPS 2024Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
132. NeurIPS 2024Iterative Methods via Locally Evolving Set Process
133. NeurIPS 2024John Ellipsoids via Lazy Updates
134. NeurIPS 2024Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
135. NeurIPS 2024Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
136. NeurIPS 2024Learnability of high-dimensional targets by two-parameter models and gradient flow
137. NeurIPS 2024Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
138. NeurIPS 2024Learning Cut Generating Functions for Integer Programming
139. NeurIPS 2024Learning diffusion at lightspeed
140. NeurIPS 2024Learning Elastic Costs to Shape Monge Displacements
141. NeurIPS 2024Learning from Noisy Labels via Conditional Distributionally Robust Optimization
142. NeurIPS 2024Learning Generalized Linear Programming Value Functions
143. NeurIPS 2024Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
144. NeurIPS 2024Learning to Handle Complex Constraints for Vehicle Routing Problems
145. NeurIPS 2024Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
146. NeurIPS 2024Learning via Surrogate PAC-Bayes
147. NeurIPS 2024Learning with Fitzpatrick Losses
148. NeurIPS 2024Learning-Augmented Algorithms for the Bahncard Problem
149. NeurIPS 2024Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems
150. NeurIPS 2024Learning-Augmented Dynamic Submodular Maximization

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.