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

318 papers, page 2 of 7

51. NeurIPS 2025Can Diffusion Models Disentangle? A Theoretical Perspective
52. NeurIPS 2025CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
53. NeurIPS 2025Combinatorial Ski Rental Problem: Robust and Learning-Augmented Algorithms
54. NeurIPS 2025Conditional Gradient Methods with Standard LMO for Stochastic Simple Bilevel Optimization
55. NeurIPS 2025Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
56. NeurIPS 2025Constrained Linear Thompson Sampling
57. NeurIPS 2025Constrained Optimization From a Control Perspective via Feedback Linearization
58. NeurIPS 2025Contextual Dynamic Pricing with Heterogeneous Buyers
59. NeurIPS 2025Continuous Subspace Optimization for Continual Learning
60. NeurIPS 2025Continuous-time Riemannian SGD and SVRG Flows on Wasserstein Probabilistic Space
61. NeurIPS 2025Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
62. NeurIPS 2025Convergence of Clipped SGD on Convex $(L_0,L_1)$-Smooth Functions
63. NeurIPS 2025Convergence of the Gradient Flow for Shallow ReLU Networks on Weakly Interacting Data
64. NeurIPS 2025Convergence Rates for Gradient Descent on the Edge of Stability for Overparametrised Least Squares
65. NeurIPS 2025Convergence Rates of Constrained Expected Improvement
66. NeurIPS 2025Coreset for Robust Geometric Median: Eliminating Size Dependency on Outliers
67. NeurIPS 2025Corporate Needs You to Find the Difference: Revisiting Submodular and Supermodular Ratio Optimization Problems
68. NeurIPS 2025Cyclic Counterfactuals under Shift–Scale Interventions
69. NeurIPS 2025Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
70. NeurIPS 2025Deep learning for continuous-time stochastic control with jumps
71. NeurIPS 2025Deep Legendre Transform
72. NeurIPS 2025Deployment Efficient Reward-Free Exploration with Linear Function Approximation
73. NeurIPS 2025DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization
74. NeurIPS 2025Differentiable extensions with rounding guarantees for combinatorial optimization over permutations
75. NeurIPS 2025Differentiation Through Black-Box Quadratic Programming Solvers
76. NeurIPS 2025Dimension-adapted Momentum Outscales SGD
77. NeurIPS 2025Disentangling Hyperedges through the Lens of Category Theory
78. NeurIPS 2025Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
79. NeurIPS 2025Distribution-Aware Tensor Decomposition for Compression of Convolutional Neural Networks
80. NeurIPS 2025Distributionally Robust Feature Selection
81. NeurIPS 2025Distributionally Robust Performative Optimization
82. NeurIPS 2025Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study
83. NeurIPS 2025Dynamic Regret Reduces to Kernelized Static Regret
84. NeurIPS 2025Effective Neural Approximations for Geometric Optimization Problems
85. NeurIPS 2025Efficient Adaptive Federated Optimization
86. NeurIPS 2025Efficient Algorithms for Robust and Partial Semi-Discrete Optimal Transport
87. NeurIPS 2025Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
88. NeurIPS 2025Efficient Quadratic Corrections for Frank-Wolfe Algorithms
89. NeurIPS 2025Efficient Spectral Control of Partially Observed Linear Dynamical Systems
90. NeurIPS 2025Efficiently Escaping Saddle Points under Generalized Smoothness via Self-Bounding Regularity
91. NeurIPS 2025Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rules
92. NeurIPS 2025Enhanced Cyclic Coordinate Descent Methods for Elastic Net Penalized Linear Models
93. NeurIPS 2025Enhancing Optimizer Stability: Momentum Adaptation of The NGN Step-size
94. NeurIPS 2025Enhancing Zero-Shot Black-Box Optimization via Pretrained Models with Efficient Population Modeling, Interaction, and Stable Gradient Approximation
95. NeurIPS 2025Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions
96. NeurIPS 2025Error Feedback under $(L_0,L_1)$-Smoothness: Normalization and Momentum
97. NeurIPS 2025Escaping saddle points without Lipschitz smoothness: the power of nonlinear preconditioning
98. NeurIPS 2025Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel–Young Losses
99. NeurIPS 2025Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
100. NeurIPS 2025Exploring Landscapes for Better Minima along Valleys

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.