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 29 of 38

1401. ICML 2024How Free is Parameter-Free Stochastic Optimization?
1402. ICML 2024How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
1403. ICML 2024Implicit Bias of AdamW: $\ell_\infty$-Norm Constrained Optimization
1404. ICML 2024Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
1405. ICML 2024Improving SAM Requires Rethinking its Optimization Formulation
1406. ICML 2024KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions
1407. ICML 2024Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
1408. ICML 2024Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
1409. ICML 2024Minimum Norm Interpolation Meets The Local Theory of Banach Spaces
1410. ICML 2024Model Alignment as Prospect Theoretic Optimization
1411. ICML 2024No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
1412. ICML 2024Non-convex Stochastic Composite Optimization with Polyak Momentum
1413. ICML 2024Non-stationary Online Convex Optimization with Arbitrary Delays
1414. ICML 2024On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
1415. ICML 2024On Statistical Learning Theory for Distributional Inputs
1416. ICML 2024On the Complexity of Finite-Sum Smooth Optimization under the Polyak–Łojasiewicz Condition
1417. ICML 2024On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
1418. ICML 2024On the Last-Iterate Convergence of Shuffling Gradient Methods
1419. ICML 2024Open Ad Hoc Teamwork with Cooperative Game Theory
1420. ICML 2024Optimization without Retraction on the Random Generalized Stiefel Manifold
1421. ICML 2024PIDformer: Transformer Meets Control Theory
1422. ICML 2024Plug-in Performative Optimization
1423. ICML 2024Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
1424. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
1425. ICML 2024Position: Optimization in SciML Should Employ the Function Space Geometry
1426. ICML 2024Practical Hamiltonian Monte Carlo on Riemannian Manifolds via Relativity Theory
1427. ICML 2024Principled Preferential Bayesian Optimization
1428. ICML 2024Proactive DP: A Multiple Target Optimization Framework for DP-SGD
1429. ICML 2024Quantum Algorithm for Online Exp-concave Optimization
1430. ICML 2024Random matrix theory improved Fréchet mean of symmetric positive definite matrices
1431. ICML 2024Relational Learning in Pre-Trained Models: A Theory from Hypergraph Recovery Perspective
1432. ICML 2024Risk Aware Benchmarking of Large Language Models
1433. ICML 2024Robust Data-driven Prescriptiveness Optimization
1434. ICML 2024Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
1435. ICML 2024Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Loss
1436. ICML 2024Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
1437. ICML 2024Stochastic Weakly Convex Optimization beyond Lipschitz Continuity
1438. ICML 2024The Max-Min Formulation of Multi-Objective Reinforcement Learning: From Theory to a Model-Free Algorithm
1439. ICML 2024Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms
1440. ICML 2024Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
1441. ICML 2024Tuning-Free Stochastic Optimization
1442. ICML 2024Two-timescale Derivative Free Optimization for Performative Prediction with Markovian Data
1443. ICML 2024Universal Gradient Methods for Stochastic Convex Optimization
1444. ICML 2024Vanilla Bayesian Optimization Performs Great in High Dimensions
1445. NeurIPS 2024$\boldsymbol{\mu}\mathbf{P^2}$: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
1446. NeurIPS 20244+3 Phases of Compute-Optimal Neural Scaling Laws
1447. NeurIPS 2024A Combinatorial Algorithm for the Semi-Discrete Optimal Transport Problem
1448. NeurIPS 2024A Framework for Bilevel Optimization on Riemannian Manifolds
1449. NeurIPS 2024A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization
1450. NeurIPS 2024A Primal-Dual-Assisted Penalty Approach to Bilevel Optimization with Coupled Constraints

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.