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 10 of 16

451. NeurIPS 2024Adam with model exponential moving average is effective for nonconvex optimization
452. NeurIPS 2024Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
453. NeurIPS 2024Adaptive Proximal Gradient Method for Convex Optimization
454. NeurIPS 2024Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions
455. NeurIPS 2024Adversarially Robust Dense-Sparse Tradeoffs via Heavy-Hitters
456. NeurIPS 2024Algebraic Positional Encodings
457. NeurIPS 2024ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Models
458. NeurIPS 2024An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
459. NeurIPS 2024An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
460. NeurIPS 2024An Improved Empirical Fisher Approximation for Natural Gradient Descent
461. NeurIPS 2024Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering
462. NeurIPS 2024Approximating the Top Eigenvector in Random Order Streams
463. NeurIPS 2024Are Multiple Instance Learning Algorithms Learnable for Instances?
464. NeurIPS 2024B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
465. NeurIPS 2024BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models
466. NeurIPS 2024Bayes-optimal learning of an extensive-width neural network from quadratically many samples
467. NeurIPS 2024Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization
468. NeurIPS 2024Bayesian Optimization of Functions over Node Subsets in Graphs
469. NeurIPS 2024Bayesian Strategic Classification
470. NeurIPS 2024Benign overfitting in leaky ReLU networks with moderate input dimension
471. NeurIPS 2024Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training
472. NeurIPS 2024Binary Search with Distributional Predictions
473. NeurIPS 2024Boundary Decomposition for Nadir Objective Vector Estimation
474. NeurIPS 2024BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
475. NeurIPS 2024Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences
476. NeurIPS 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
477. NeurIPS 2024Can neural operators always be continuously discretized?
478. NeurIPS 2024Catastrophic Goodhart: regularizing RLHF with KL divergence does not mitigate heavy-tailed reward misspecification
479. NeurIPS 2024Clustering with Non-adaptive Subset Queries
480. NeurIPS 2024CoBo: Collaborative Learning via Bilevel Optimization
481. NeurIPS 2024Collaboration! Towards Robust Neural Methods for Routing Problems
482. NeurIPS 2024Communication-Efficient Federated Group Distributionally Robust Optimization
483. NeurIPS 2024Computing the Bias of Constant-step Stochastic Approximation with Markovian Noise
484. NeurIPS 2024Conformal Inverse Optimization
485. NeurIPS 2024Constrained Sampling with Primal-Dual Langevin Monte Carlo
486. NeurIPS 2024Contextual Linear Optimization with Bandit Feedback
487. NeurIPS 2024Continuous Partitioning for Graph-Based Semi-Supervised Learning
488. NeurIPS 2024Contracting with a Learning Agent
489. NeurIPS 2024Controlling Continuous Relaxation for Combinatorial Optimization
490. NeurIPS 2024Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
491. NeurIPS 2024Convergence Analysis of Split Federated Learning on Heterogeneous Data
492. NeurIPS 2024Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods
493. NeurIPS 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
494. NeurIPS 2024Credal Learning Theory
495. NeurIPS 2024CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
496. NeurIPS 2024Decision-Focused Learning with Directional Gradients
497. NeurIPS 2024Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond
498. NeurIPS 2024Deep linear networks for regression are implicitly regularized towards flat minima
499. NeurIPS 2024Derivatives of Stochastic Gradient Descent in parametric optimization
500. NeurIPS 2024Diffeomorphic interpolation for efficient persistence-based topological 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.