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

494 papers, page 4 of 10

151. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
152. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
153. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
154. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
155. ICML 2024Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
156. ICML 2024Model Alignment as Prospect Theoretic Optimization
157. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
158. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
159. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
160. ICML 2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
161. ICML 2024On Statistical Learning Theory for Distributional Inputs
162. ICML 2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
163. ICML 2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
164. ICML 2024On the Last-Iterate Convergence of Shuffling Gradient Methods
165. ICML 2024Open Ad Hoc Teamwork with Cooperative Game Theory
166. ICML 2024Optimization without Retraction on the Random Generalized Stiefel Manifold
167. ICML 2024PIDformer: Transformer Meets Control Theory
168. ICML 2024Plug-in Performative Optimization
169. ICML 2024Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
170. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
171. ICML 2024Position: Optimization in SciML Should Employ the Function Space Geometry
172. ICML 2024Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
173. ICML 2024Principled Preferential Bayesian Optimization
174. ICML 2024Proactive DP: A Multiple Target Optimization Framework for DP-SGD
175. ICML 2024Quantum Algorithm for Online Exp-concave Optimization
176. ICML 2024Random matrix theory improved Fréchet mean of symmetric positive definite matrices
177. ICML 2024Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
178. ICML 2024Risk Aware Benchmarking of Large Language Models
179. ICML 2024Robust Data-driven Prescriptiveness Optimization
180. ICML 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
181. ICML 2024Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
182. ICML 2024Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
183. ICML 2024Stochastic Weakly Convex Optimization beyond Lipschitz Continuity
184. ICML 2024The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
185. ICML 2024Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
186. ICML 2024Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
187. ICML 2024Tuning-Free Stochastic Optimization
188. ICML 2024Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
189. ICML 2024Universal Gradient Methods for Stochastic Convex Optimization
190. ICML 2024Vanilla Bayesian Optimization Performs Great in High Dimensions
191. NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
192. NeurIPS 20244+3 Phases of Compute-Optimal Neural Scaling Laws
193. NeurIPS 2024A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem
194. NeurIPS 2024A Framework for Bilevel Optimization on Riemannian Manifolds
195. NeurIPS 2024A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization
196. NeurIPS 2024A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints
197. NeurIPS 2024A robust inlier identification algorithm for point cloud registration via $\mathbf{\ell_0}$-minimization
198. NeurIPS 2024A two-scale Complexity Measure for Deep Learning Models
199. NeurIPS 2024Accelerating Matroid Optimization through Fast Imprecise Oracles
200. NeurIPS 2024Accelerating Non-Maximum Suppression: A Graph Theory Perspective

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.