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 35 of 38

1701. NeurIPS 2024Stepwise Alignment for Constrained Language Model Policy Optimization
1702. NeurIPS 2024Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
1703. NeurIPS 2024Stochastic Newton Proximal Extragradient Method
1704. NeurIPS 2024Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss
1705. NeurIPS 2024Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity
1706. NeurIPS 2024Super Consistency of Neural Network Landscapes and Learning Rate Transfer
1707. NeurIPS 2024SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization
1708. NeurIPS 2024Symmetric Linear Bandits with Hidden Symmetry
1709. NeurIPS 2024Testably Learning Polynomial Threshold Functions
1710. NeurIPS 2024The Collusion of Memory and Nonlinearity in Stochastic Approximation With Constant Stepsize
1711. NeurIPS 2024The Evolution of Statistical Induction Heads: In-Context Learning Markov Chains
1712. NeurIPS 2024The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
1713. NeurIPS 2024The Implicit Bias of Gradient Descent on Separable Multiclass Data
1714. NeurIPS 2024The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing
1715. NeurIPS 2024The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information
1716. NeurIPS 2024The Many Faces of Optimal Weak-to-Strong Learning
1717. NeurIPS 2024The Power of Extrapolation in Federated Learning
1718. NeurIPS 2024The Road Less Scheduled
1719. NeurIPS 2024The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
1720. NeurIPS 2024The Secretary Problem with Predicted Additive Gap
1721. NeurIPS 2024The Space Complexity of Approximating Logistic Loss
1722. NeurIPS 2024The Star Geometry of Critic-Based Regularizer Learning
1723. NeurIPS 2024There is No Silver Bullet: Benchmarking Methods in Predictive Combinatorial Optimization
1724. NeurIPS 2024Tight Bounds for Learning RUMs from Small Slates
1725. NeurIPS 2024Tighter Convergence Bounds for Shuffled SGD via Primal-Dual Perspective
1726. NeurIPS 2024Tolerant Algorithms for Learning with Arbitrary Covariate Shift
1727. NeurIPS 2024Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms
1728. NeurIPS 2024Topological obstruction to the training of shallow ReLU neural networks
1729. NeurIPS 2024TorchOpt: An Efficient Library for Differentiable Optimization
1730. NeurIPS 2024Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models
1731. NeurIPS 2024Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
1732. NeurIPS 2024Towards Exact Gradient-based Training on Analog In-memory Computing
1733. NeurIPS 2024Towards Scalable and Stable Parallelization of Nonlinear RNNs
1734. NeurIPS 2024Trade-Offs of Diagonal Fisher Information Matrix Estimators
1735. NeurIPS 2024Training Dynamics of Transformers to Recognize Word Co-occurrence via Gradient Flow Analysis
1736. NeurIPS 2024Transductive Learning is Compact
1737. NeurIPS 2024Transition Constrained Bayesian Optimization via Markov Decision Processes
1738. NeurIPS 2024Universal Rates of Empirical Risk Minimization
1739. NeurIPS 2024Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction
1740. NeurIPS 2024Unraveling the Gradient Descent Dynamics of Transformers
1741. NeurIPS 2024Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
1742. NeurIPS 2024UQ-Guided Hyperparameter Optimization for Iterative Learners
1743. NeurIPS 2024Variance estimation in compound decision theory under boundedness
1744. NeurIPS 2024Warm-starting Push-Relabel
1745. NeurIPS 2024Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes
1746. NeurIPS 2024Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML
1747. NeurIPS 2024Why Do We Need Weight Decay in Modern Deep Learning?
1748. NeurIPS 2024Why Transformers Need Adam: A Hessian Perspective
1749. CVPR 2023Deep Hashing With Minimal-Distance-Separated Hash Centers
1750. CVPR 2023Elastic Aggregation for Federated 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.