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 32 of 38

1551. NeurIPS 2024Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
1552. NeurIPS 2024Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame
1553. NeurIPS 2024Hardness of Learning Neural Networks under the Manifold Hypothesis
1554. NeurIPS 2024High-probability complexity bounds for stochastic non-convex minimax optimization
1555. NeurIPS 2024How Does Black-Box Impact the Learning Guarantee of Stochastic Compositional Optimization?
1556. NeurIPS 2024How does Gradient Descent Learn Features --- A Local Analysis for Regularized Two-Layer Neural Networks
1557. NeurIPS 2024How Sparse Can We Prune A Deep Network: A Fundamental Limit Perspective
1558. NeurIPS 2024How to Boost Any Loss Function
1559. NeurIPS 2024How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression
1560. NeurIPS 2024HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links
1561. NeurIPS 2024Implicit Bias of Mirror Flow on Separable Data
1562. NeurIPS 2024Implicit Regularization of Decentralized Gradient Descent for Sparse Regression
1563. NeurIPS 2024Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
1564. NeurIPS 2024Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces
1565. NeurIPS 2024Improved Particle Approximation Error for Mean Field Neural Networks
1566. NeurIPS 2024Improved Sample Complexity for Multiclass PAC Learning
1567. NeurIPS 2024Improving Equivariant Model Training via Constraint Relaxation
1568. NeurIPS 2024Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
1569. NeurIPS 2024In-Context Learning of a Linear Transformer Block: Benefits of the MLP Component and One-Step GD Initialization
1570. NeurIPS 2024Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques
1571. NeurIPS 2024Inexact Augmented Lagrangian Methods for Conic Optimization: Quadratic Growth and Linear Convergence
1572. NeurIPS 2024Infinite Limits of Multi-head Transformer Dynamics
1573. NeurIPS 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
1574. NeurIPS 2024IPM-LSTM: A Learning-Based Interior Point Method for Solving Nonlinear Programs
1575. NeurIPS 2024Is Cross-validation the Gold Standard to Estimate Out-of-sample Model Performance?
1576. NeurIPS 2024Iterative Methods via Locally Evolving Set Process
1577. NeurIPS 2024John Ellipsoids via Lazy Updates
1578. NeurIPS 2024Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization
1579. NeurIPS 2024Last-Iterate Convergence for Generalized Frank-Wolfe in Monotone Variational Inequalities
1580. NeurIPS 2024Learnability of high-dimensional targets by two-parameter models and gradient flow
1581. NeurIPS 2024Learning and Transferring Sparse Contextual Bigrams with Linear Transformers
1582. NeurIPS 2024Learning Cut Generating Functions for Integer Programming
1583. NeurIPS 2024Learning diffusion at lightspeed
1584. NeurIPS 2024Learning Elastic Costs to Shape Monge Displacements
1585. NeurIPS 2024Learning from Noisy Labels via Conditional Distributionally Robust Optimization
1586. NeurIPS 2024Learning Generalized Linear Programming Value Functions
1587. NeurIPS 2024Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random
1588. NeurIPS 2024Learning to Handle Complex Constraints for Vehicle Routing Problems
1589. NeurIPS 2024Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
1590. NeurIPS 2024Learning via Surrogate PAC-Bayes
1591. NeurIPS 2024Learning with Fitzpatrick Losses
1592. NeurIPS 2024Learning-Augmented Algorithms for the Bahncard Problem
1593. NeurIPS 2024Learning-Augmented Approximation Algorithms for Maximum Cut and Related Problems
1594. NeurIPS 2024Learning-Augmented Dynamic Submodular Maximization
1595. NeurIPS 2024Length Optimization in Conformal Prediction
1596. NeurIPS 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
1597. NeurIPS 2024Linear Time Approximation Algorithm for Column Subset Selection with Local Search
1598. NeurIPS 2024Loss Landscape Characterization of Neural Networks without Over-Parametrization
1599. NeurIPS 2024Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks
1600. NeurIPS 2024Lower Bounds of Uniform Stability in Gradient-Based Bilevel Algorithms for Hyperparameter 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.