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

778 papers, page 6 of 16

251. NeurIPS 2025Hierarchical Demonstration Order Optimization for Many-shot In-Context Learning
252. NeurIPS 2025Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems
253. NeurIPS 2025High-Dimensional Calibration from Swap Regret
254. NeurIPS 2025High-Performance Arithmetic Circuit Optimization via Differentiable Architecture Search
255. NeurIPS 2025How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?
256. NeurIPS 2025How Memory in Optimization Algorithms Implicitly Modifies the Loss
257. NeurIPS 2025How Particle System Theory Enhances Hypergraph Message Passing
258. NeurIPS 2025How to Learn a Star: Binary Classification with Starshaped Polyhedral Sets
259. NeurIPS 2025How to Scale Second-Order Optimization
260. NeurIPS 2025Hybrid-Balance GFlowNet for Solving Vehicle Routing Problems
261. NeurIPS 2025IMPACT: Irregular Multi-Patch Adversarial Composition Based on Two‑Phase Optimization
262. NeurIPS 2025Improved Algorithms for Fair Matroid Submodular Maximization
263. NeurIPS 2025Improved Balanced Classification with Theoretically Grounded Loss Functions
264. NeurIPS 2025Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
265. NeurIPS 2025Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learning
266. NeurIPS 2025Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring
267. NeurIPS 2025Isotropic Noise in Stochastic and Quantum Convex Optimization
268. NeurIPS 2025Kernel von Mises Formula of the Influence Function
269. NeurIPS 2025Knee-Deep in C-RASP: A Transformer Depth Hierarchy
270. NeurIPS 2025KOALA++: Efficient Kalman-Based Optimization with Gradient-Covariance Products
271. NeurIPS 2025Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression
272. NeurIPS 2025Last Iterate Convergence in Monotone Mean Field Games
273. NeurIPS 2025Learning (Approximately) Equivariant Networks via Constrained Optimization
274. NeurIPS 2025Learning Cocoercive Conservative Denoisers via Helmholtz Decomposition for Poisson Imaging Inverse Problems
275. NeurIPS 2025Learning Expandable and Adaptable Representations for Continual Learning
276. NeurIPS 2025Learning from A Single Markovian Trajectory: Optimality and Variance Reduction
277. NeurIPS 2025Learning from Interval Targets
278. NeurIPS 2025Learning Linear Attention in Polynomial Time
279. NeurIPS 2025Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
280. NeurIPS 2025Learning Provably Improves the Convergence of Gradient Descent
281. NeurIPS 2025Learning Theory for Kernel Bilevel Optimization
282. NeurIPS 2025Learning to Insert for Constructive Neural Vehicle Routing Solver
283. NeurIPS 2025Learning with Statistical Equality Constraints
284. NeurIPS 2025Length Generalization via Auxiliary Tasks
285. NeurIPS 2025Local Curvature Descent: Squeezing More Curvature out of Standard and Polyak Gradient Descent
286. NeurIPS 2025Machine Unlearning under Overparameterization
287. NeurIPS 2025MAP Estimation with Denoisers: Convergence Rates and Guarantees
288. NeurIPS 2025Marginal-Nonuniform PAC Learnability
289. NeurIPS 2025Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains
290. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
291. NeurIPS 2025Metric Automata Theory: A Unifying Theory of RNNs
292. NeurIPS 2025MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
293. NeurIPS 2025MISA: Memory-Efficient LLMs Optimization with Module-wise Importance Sampling
294. NeurIPS 2025Mitigating Hallucination Through Theory-Consistent Symmetric Multimodal Preference Optimization
295. NeurIPS 2025Modality-Aware SAM: Sharpness-Aware-Minimization Driven Gradient Modulation for Harmonized Multimodal Learning
296. NeurIPS 2025MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
297. NeurIPS 2025Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
298. NeurIPS 2025NaDRO: Leveraging Dual-Reward Strategies for LLMs Training on Noisy Data
299. NeurIPS 2025Natural Gradient VI: Guarantees for Non-Conjugate Models
300. NeurIPS 2025Neural Collapse under Gradient Flow on Shallow ReLU Networks for Orthogonally Separable Data

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.