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

214 papers, page 3 of 5

101. ICML 2026Learning Dynamics of Zeroth-Order Optimization: A Kernel Perspective
102. ICML 2026Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent
103. ICML 2026Learning Rate Annealing Improves Tuning Robustness in Stochastic Optimization
104. ICML 2026Learning the Best Under Constraints: A Duality-Based Framework
105. ICML 2026Learning-Augmented Online Covering Problems
106. ICML 2026Learning-Augmented Online Minimization with Dual Predictions
107. ICML 2026Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases
108. ICML 2026Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction
109. ICML 2026Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noises
110. ICML 2026LoBCD-GW: A Fast and Data-Dependent Algorithm for Computing Gromov-Wasserstein Distance via Localized Block Coordinate Descent
111. ICML 2026Local Minima in Quadratic-Penalty Relaxations of Binary Linear Programs
112. ICML 2026Logarithmic Switching Regret for Online Convex Optimization
113. ICML 2026Low-dimensional topology of deep neural networks
114. ICML 2026Lower Complexity Bounds for Nonconvex-Strongly-Convex Bilevel Optimization with First-Order Oracles
115. ICML 2026LOZO+: Provably Efficient Zeroth-Order Fine-Tuning via Greedy Low-Rank Subspace Selection
116. ICML 2026M+Adam: Low-Precision Training via Additive–Multiplicative Optimization
117. ICML 2026Matroid Algorithms Under Size-Sensitive Independence Oracles
118. ICML 2026Maximin Relative Improvement: Fair Learning as a Bargaining Problem
119. ICML 2026Mirror Descent Under Generalized Smoothness
120. ICML 2026Mirror Mean-Field Langevin Dynamics
121. ICML 2026Mixtures Closest To A Given Measure: A Semidefinite Programming Approach
122. ICML 2026Mixtures of geodesic factor analyzers on Riemannian homogeneous spaces
123. ICML 2026Momentum Further Constrains Sharpness at the Edge of Stochastic Stability
124. ICML 2026Multicalibration Yields Better Matchings
125. ICML 2026Natural Hypergradient Descent: Algorithm Design, Convergence Analysis, and Parallel Implementation
126. ICML 2026Near-Optimal and Efficient First-Order Algorithm for Multi-Task Learning with Shared Linear Representation
127. ICML 2026Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
128. ICML 2026Non-Euclidean Gradient Descent Operates at the Edge of Stability
129. ICML 2026Offline Two-Player Zero-Sum Markov Games with KL Regularization
130. ICML 2026On Expressive Power of Floating-Point Transformers
131. ICML 2026On Regret Bounds of Thompson Sampling for Bayesian Optimization
132. ICML 2026On the Computational Complexity of Performative Prediction
133. ICML 2026On the Convergence Rate of LoRA Gradient Descent
134. ICML 2026On the Optimization Trajectory of DeepWalk Embeddings
135. ICML 2026On the Provable Suboptimality of Momentum SGD in Nonstationary Stochastic Optimization
136. ICML 2026On the Role of Batch Size in Stochastic Conditional Gradient Methods
137. ICML 2026On the Variability of Concept Activation Vectors
138. ICML 2026Operator Splitting with Hamilton-Jacobi-based Proximals
139. ICML 2026OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling
140. ICML 2026Optimal Anytime Algorithms for Online Convex Optimization with Adversarial Constraints
141. ICML 2026Optimal Regret for Policy Optimization in Contextual Bandits
142. ICML 2026Optimal Transport with Symmetry Groups
143. ICML 2026Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks
144. ICML 2026Optimized Deferral for Imbalanced Settings
145. ICML 2026OptMaster: A DAG-Based Framework for Formulation and Heuristic Discovery in Optimization
146. ICML 2026OVLR: Efficient, Scalable, and Robust Training via Output-Level Variance-Reduced Likelihood Ratio
147. ICML 2026Parameter-Masked Decoupled Optimization for Cross-Domain Class-Incremental Learning
148. ICML 2026Performative Learning Theory
149. ICML 2026Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models
150. ICML 2026Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming

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.