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 8 of 16

351. NeurIPS 2025Product Distribution Learning with Imperfect Advice
352. NeurIPS 2025Provable Meta-Learning with Low-Rank Adaptations
353. NeurIPS 2025Purest Quantum State Identification
354. NeurIPS 2025Purity Law for Neural Routing Problem Solvers with Enhanced Generalizability
355. NeurIPS 2025Quantum Speedups for Minimax Optimization and Beyond
356. NeurIPS 2025Quasi-Self-Concordant Optimization with $\ell_{\infty}$ Lewis Weights
357. NeurIPS 2025Reinforcement learning for one-shot DAG scheduling with comparability identification and dense reward
358. NeurIPS 2025Replicable Distribution Testing
359. NeurIPS 2025Replicable Online Learning
360. NeurIPS 2025Rethinking Entropy in Test-Time Adaptation: The Missing Piece from Energy Duality
361. NeurIPS 2025Rethinking Gradient Step Denoiser: Towards Truly Pseudo-Contractive Operator
362. NeurIPS 2025Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
363. NeurIPS 2025Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency
364. NeurIPS 2025Revisiting Agnostic Boosting
365. NeurIPS 2025Revisiting Consensus Error: A Fine-grained Analysis of Local SGD under Second-order Data Heterogeneity
366. NeurIPS 2025Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
367. NeurIPS 2025Revisiting Frank-Wolfe for Structured Nonconvex Optimization
368. NeurIPS 2025Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
369. NeurIPS 2025Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency
370. NeurIPS 2025Robustly Learning Monotone Single-Index Models
371. NeurIPS 2025SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures
372. NeurIPS 2025Sample-Adaptivity Tradeoff in On-Demand Sampling
373. NeurIPS 2025Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
374. NeurIPS 2025Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf’s Law
375. NeurIPS 2025Second-order Optimization under Heavy-Tailed Noise: Hessian Clipping and Sample Complexity Limits
376. NeurIPS 2025Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing
377. NeurIPS 2025Semi-infinite Nonconvex Constrained Min-Max Optimization
378. NeurIPS 2025Set Smoothness Unlocks Clarke Hyper-stationarity in Bilevel Optimization
379. NeurIPS 2025Sharp Matrix Empirical Bernstein Inequalities
380. NeurIPS 2025Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings
381. NeurIPS 2025Sketched Adaptive Distributed Deep Learning: A Sharp Convergence Analysis
382. NeurIPS 2025Small Singular Values Matter: A Random Matrix Analysis of Transformer Models
383. NeurIPS 2025Smart Surrogate Losses for Contextual Stochastic Linear Optimization with Robust Constraints
384. NeurIPS 2025Smoothed Agnostic Learning of Halfspaces over the Hypercube
385. NeurIPS 2025Solver-Free Decision-Focused Learning for Linear Optimization Problems
386. NeurIPS 2025Solver-Informed RL: Grounding Large Language Models for Authentic Optimization Modeling
387. NeurIPS 2025Solving Neural Min-Max Games: The Role of Architecture, Initialization & Dynamics
388. NeurIPS 2025Solving the Asymmetric Traveling Salesman Problem via Trace-Guided Cost Augmentation
389. NeurIPS 2025Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
390. NeurIPS 2025Sparse Polyak: an adaptive step size rule for high-dimensional M-estimation
391. NeurIPS 2025Spectral Estimation with Free Decompression
392. NeurIPS 2025Stab-SGD: Noise-Adaptivity in Smooth Optimization with Stability Ratios
393. NeurIPS 2025Stable Coresets via Posterior Sampling: Aligning Induced and Full Loss Landscapes
394. NeurIPS 2025Stochastic Gradients under Nuisances
395. NeurIPS 2025Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization
396. NeurIPS 2025Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
397. NeurIPS 2025Streaming Attention Approximation via Discrepancy Theory
398. NeurIPS 2025Streaming Federated Learning with Markovian Data
399. NeurIPS 2025Streaming Stochastic Submodular Maximization with On-Demand User Requests
400. NeurIPS 2025System Prompt Optimization with Meta-Learning

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.