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 11 of 38

501. NeurIPS 2026Fixed Universal Transformers
502. NeurIPS 2026Follow the Regularized Leader Does Not Converge in Constrained Optimization
503. NeurIPS 2026Foundations of Categorical Equivariant Deep Learning
504. NeurIPS 2026From Cursed to Competitive: Closing the ZO–FO Gap via Input-to-State Stability
505. NeurIPS 2026From SGD to Muon: Adaptive Optimization via Schatten-p Norms
506. NeurIPS 2026Functional Gradient Descent with Adaptive Representations
507. NeurIPS 2026Generalization Without Compression Penalty: A Stability Analysis of Error Feedback
508. NeurIPS 2026Generating in the Limit with Infinitely Many Hallucinations
509. NeurIPS 2026Global Convergence in Deep Networks via the Second Law of Thermodynamics
510. NeurIPS 2026Gradient Regularized Newton Boosting Trees with Global Convergence
511. NeurIPS 2026Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes
512. NeurIPS 2026Group Distributionally Robust Optimization with Flexible Sample Queries
513. NeurIPS 2026Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold
514. NeurIPS 2026High dimensional theory of two-phase optimizers
515. NeurIPS 2026High-arity Sample Compression
516. NeurIPS 2026How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization
517. NeurIPS 2026Imperfect Influence, Reliable Rankings: A Theory of TRAK for Data Attribution
518. NeurIPS 2026Improved Regret Analysis For Parallel Gaussian Process Bandit Optimization
519. NeurIPS 2026Improving the Optimization Landscape of Matrix Completion with $\epsilon$-close Surrogates
520. NeurIPS 2026In-Context Black-Box Optimization with Unreliable Feedback
521. NeurIPS 2026In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
522. NeurIPS 2026Interpreting Neural Combinatorial Optimization via Evolving Programmatic Bottlenecks
523. NeurIPS 2026Intrinsic Muon: Spectral Optimization on Riemannian Matrix Manifolds
524. NeurIPS 2026KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Task
525. NeurIPS 2026Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
526. NeurIPS 2026Learning Acceptable Lotteries via Queries: Minimizing Aggregated Violation Distances
527. NeurIPS 2026Learning Polyhedral Conformal Sets for Robust Optimization
528. NeurIPS 2026Learning Rate Decay Can Exponentially Accelerate SGD for Global Optimization of Nonconvex Functions
529. NeurIPS 2026Learning to Audit ML Models with Theory of Mind
530. NeurIPS 2026Learning to Discover Iterative Spectral Algorithms
531. NeurIPS 2026Linear Contextual Bandits with Quasi-Optimism
532. NeurIPS 2026Linear Ensemble Sampling with Fewer Ensembles
533. NeurIPS 2026Locally-Additive Regret for Delayed Non-stationary Bandit Convex Optimization
534. NeurIPS 2026Look Before You Leap: Self-Evolving Clinical Reasoning with Psychometric Preference Optimization for Radiology Report Generation
535. NeurIPS 2026LOTION: Smoothing the Optimization Landscape for Quantized Training
536. NeurIPS 2026Masked Sobolev Training for Feasibility-Reliable Optimization Proxies
537. NeurIPS 2026Median-of-Means under Structured Heavy-Tailed Noise: High-Probability Bounds for Clipped Stochastic Optimization
538. NeurIPS 2026Memory Determines Learning Direction: A Theory of Gradient-Based Optimization in State Space Models
539. NeurIPS 2026Minimax Optimization without Spurious Solutions in Optimal Transport Learning
540. NeurIPS 2026MinMax Recurrent Neural Cascades
541. NeurIPS 2026Model Collapse is a Singular Complexity Trajectory
542. NeurIPS 2026Nerve-Skeleton Message Passing for Federated Optimization with Overlapping Parameters
543. NeurIPS 2026Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise
544. NeurIPS 2026Objective Shaping with Hard Negatives: Windowed Partial AUC Optimization for RL-based LLM Recommenders
545. NeurIPS 2026On the Convergence of Success Conditioning for Policy Optimization
546. NeurIPS 2026On the Last-Iterate Convergence of Clipped Gradient Methods
547. NeurIPS 2026On the Parallel Optimality of Exponentiated Gradient Descent
548. NeurIPS 2026Online Min-Max Optimization: From Individual Regrets to Cumulative Saddle Points
549. NeurIPS 2026Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms
550. NeurIPS 2026PACE: Partial-state Amortized Constraint Editing for Neural Combinatorial 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.