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

1,871 papers, page 23 of 38

1101. NeurIPS 2025Length Generalization via Auxiliary Tasks
1102. NeurIPS 2025Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent
1103. NeurIPS 2025Machine Unlearning under Overparameterization
1104. NeurIPS 2025MAP Estimation with Denoisers: Convergence Rates and Guarantees
1105. NeurIPS 2025Marginal-Nonuniform PAC Learnability
1106. NeurIPS 2025Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains
1107. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
1108. NeurIPS 2025Metric Automata Theory: A Unifying Theory of RNNs
1109. NeurIPS 2025MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
1110. NeurIPS 2025MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling
1111. NeurIPS 2025Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization
1112. NeurIPS 2025Modality-Aware SAM: Sharpness-Aware-Minimization Driven Gradient Modulation for Harmonized Multimodal Learning
1113. NeurIPS 2025MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
1114. NeurIPS 2025Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
1115. NeurIPS 2025NaDRO: Leveraging Dual-Reward Strategies for LLMs Training on Noisy Data
1116. NeurIPS 2025Natural Gradient VI: Guarantees for Non-Conjugate Models
1117. NeurIPS 2025Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data
1118. NeurIPS 2025Neural Combinatorial Optimization for Time Dependent Traveling Salesman Problem
1119. NeurIPS 2025Neural Evolution Strategy for Black-box Pareto Set Learning
1120. NeurIPS 2025New Parallel and Streaming Algorithms for Directed Densest Subgraph
1121. NeurIPS 2025New Perspectives on the Polyak Stepsize: Surrogate Functions and Negative Results
1122. NeurIPS 2025Non-Clairvoyant Scheduling with Progress Bars
1123. NeurIPS 2025Non-convex entropic mean-field optimization via Best Response flow
1124. NeurIPS 2025Non-Convex Tensor Recovery from Tube-Wise Sensing
1125. NeurIPS 2025Non-monotone Submodular Optimization: $p$-Matchoid Constraints and Fully Dynamic Setting
1126. NeurIPS 2025Non-Singularity of the Gradient Descent Map for Neural Networks with Piecewise Analytic Activations
1127. NeurIPS 2025Non-stationary Bandit Convex Optimization: A Comprehensive Study
1128. NeurIPS 2025Non-Stationary Lipschitz Bandits
1129. NeurIPS 2025Nonlinear Laplacians: Tunable principal component analysis under directional prior information
1130. NeurIPS 2025Nonlinearly Preconditioned Gradient Methods: Momentum and Stochastic Analysis
1131. NeurIPS 2025NPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
1132. NeurIPS 2025Nyström-Accelerated Primal LS-SVMs: Breaking the $O(an^3)$ Complexity Bottleneck for Scalable ODEs Learning
1133. NeurIPS 2025On Extending Direct Preference Optimization to Accommodate Ties
1134. NeurIPS 2025On Linear Mode Connectivity of Mixture-of-Experts Architectures
1135. NeurIPS 2025On the $O(\frac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm
1136. NeurIPS 2025On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
1137. NeurIPS 2025On the Convergence of Stochastic Smoothed Multi-Level Compositional Gradient Descent Ascent
1138. NeurIPS 2025On the Emergence of Linear Analogies in Word Embeddings
1139. NeurIPS 2025On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
1140. NeurIPS 2025On the Robustness of Transformers against Context Hijacking for Linear Classification
1141. NeurIPS 2025On the sample complexity of semi-supervised multi-objective learning
1142. NeurIPS 2025On Traceability in $\ell_p$ Stochastic Convex Optimization
1143. NeurIPS 2025On Transferring Transferability: Towards a Theory for Size Generalization
1144. NeurIPS 2025On Universality Classes of Equivariant Networks
1145. NeurIPS 2025Online Inverse Linear Optimization: Efficient Logarithmic-Regret Algorithm, Robustness to Suboptimality, and Lower Bound
1146. NeurIPS 2025Online Multi-Class Selection with Group Fairness Guarantee
1147. NeurIPS 2025Online Portfolio Selection with ML Predictions
1148. NeurIPS 2025Online Two-Stage Submodular Maximization
1149. NeurIPS 2025Opinion Maximization in Social Networks by Modifying Internal Opinions
1150. NeurIPS 2025OPTFM: A Scalable Multi-View Graph Transformer for Hierarchical Pre-Training in Combinatorial 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.