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 21 of 38

1001. NeurIPS 2025Convergence Rates of Constrained Expected Improvement
1002. NeurIPS 2025Coreset for Robust Geometric Median: Eliminating Size Dependency on Outliers
1003. NeurIPS 2025Corporate Needs You to Find the Difference: Revisiting Submodular and Supermodular Ratio Optimization Problems
1004. NeurIPS 2025Cyclic Counterfactuals under Shift–Scale Interventions
1005. NeurIPS 2025Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
1006. NeurIPS 2025Deep learning for continuous-time stochastic control with jumps
1007. NeurIPS 2025Deep Legendre Transform
1008. NeurIPS 2025Deployment Efficient Reward-Free Exploration with Linear Function Approximation
1009. NeurIPS 2025DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
1010. NeurIPS 2025Differentiable extensions with rounding guarantees for combinatorial optimization over permutations
1011. NeurIPS 2025Differentiation Through Black-Box Quadratic Programming Solvers
1012. NeurIPS 2025Dimension-adapted Momentum Outscales SGD
1013. NeurIPS 2025Disentangling Hyperedges through the Lens of Category Theory
1014. NeurIPS 2025Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
1015. NeurIPS 2025Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks
1016. NeurIPS 2025Distributionally Robust Feature Selection
1017. NeurIPS 2025Distributionally Robust Performative Optimization
1018. NeurIPS 2025Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
1019. NeurIPS 2025Dynamic Regret Reduces to Kernelized Static Regret
1020. NeurIPS 2025Effective Neural Approximations for Geometric Optimization Problems
1021. NeurIPS 2025Efficient Adaptive Federated Optimization
1022. NeurIPS 2025Efficient Algorithms for Robust and Partial Semi-Discrete Optimal Transport
1023. NeurIPS 2025Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
1024. NeurIPS 2025Efficient Quadratic Corrections for Frank-Wolfe Algorithms
1025. NeurIPS 2025Efficient Spectral Control of Partially Observed Linear Dynamical Systems
1026. NeurIPS 2025Efficiently Escaping Saddle Points under Generalized Smoothness via Self-Bounding Regularity
1027. NeurIPS 2025Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
1028. NeurIPS 2025Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models
1029. NeurIPS 2025Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
1030. NeurIPS 2025Enhancing Zero-Shot Black-Box Optimization via Pretrained Models with Efficient Population Modeling, Interaction, and Stable Gradient Approximation
1031. NeurIPS 2025Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions
1032. NeurIPS 2025Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum
1033. NeurIPS 2025Escaping saddle points without Lipschitz smoothness: the power of nonlinear preconditioning
1034. NeurIPS 2025Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel–Young Losses
1035. NeurIPS 2025Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
1036. NeurIPS 2025Exploring Landscapes for Better Minima along Valleys
1037. NeurIPS 2025Extragradient Method for $(L_0, L_1)$-Lipschitz Root-finding Problems
1038. NeurIPS 2025Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
1039. NeurIPS 2025Fairness-Regularized Online Optimization with Switching Costs
1040. NeurIPS 2025Fast Last-Iterate Convergence of SGD in the Smooth Interpolation Regime
1041. NeurIPS 2025Fast Projection-Free Approach (without Optimization Oracle) for Optimization over Compact Convex Set
1042. NeurIPS 2025Fast Zeroth-Order Convex Optimization with Quantum Gradient Methods
1043. NeurIPS 2025Faster Algorithms for Structured John Ellipsoid Computation
1044. NeurIPS 2025Feasibility-Aware Decision-Focused Learning for Predicting Parameters in the Constraints
1045. NeurIPS 2025Finding Low-Rank Matrix Weights in DNNs via Riemannian Optimization: RAdaGrad and RAdamW
1046. NeurIPS 2025Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds
1047. NeurIPS 2025Flat Channels to Infinity in Neural Loss Landscapes
1048. NeurIPS 2025Follow-the-Perturbed-Leader Nearly Achieves Best-of-Both-Worlds for the m-Set Semi-Bandit Problems
1049. NeurIPS 2025Fractional Langevin Dynamics for Combinatorial Optimization via Polynomial-Time Escape
1050. NeurIPS 2025FRAM: Frobenius-Regularized Assignment Matching with Mixed-Precision Computing

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.