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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Learning Decision-Sufficient Representations for Linear Optimization COLT 2026 Learning depth-3 circuits via quantum agnostic boosting COLT 2026 Learning DFAs from Positive Examples Only via Word Counting AAAI 2026 Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract) AAAI 2026 Learning Dynamics as Feedback: An Adaptive Entropy Flow Dynamics Framework for Long-tailed Human Action Recognition AAAI 2026 Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes L4DC 2026 Learning from Answer Sets via Single-Shot Disjunctive ASP Encoding AAAI 2026 Learning from Biased and Costly Data Sources: Minimax-optimal Data Collection under a Budget (extended abstract) COLT 2026 Learning from Equivalence Queries, Revisited COLT 2026 Learning from Synthetic Data: Limitations of ERM ALT 2026 Learning from the Undesirable: Robust Adaptation of Language Models Without Forgetting AAAI 2026 Learning general conditional independence structures via the neighbourhood lattice JMLR 2026 Learning in Zero-Sum Markov Games: Relaxing Strong Reachability and Mixing Time Assumptions AAAI 2026 Learning Ising Models from Evolutions (Extended Abstract) COLT 2026 Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling AAAI 2026 Learning Periodic Strategies in Blocking Bandits Is as Hard as Bandits with Switching Costs COLT 2026 Learning plug-in surrogate endpoints for randomized experiments UAI 2026 Learning Protein–Ligand Binding in Hyperbolic Space AAAI 2026 Learning Subgroups with Maximum Treatment Effects Without Causal Heuristics AAAI 2026 Learning to Compress Graphs via Dual Agents for Consistent Topological Robustness Evaluation AAAI 2026 Learning to Ideate for Machine Learning Engineering Agents EACL 2026 Learning to Optimize Job Shop Scheduling Under Structural Uncertainty AAAI 2026 Learning to Solve Constrained Bilevel Control Co-Design Problems L4DC 2026 Learning Vision-Based Neural Network Controllers with Semi-Probabilistic Safety Guarantees AAAI 2026 Learning with Monotone Adversarial Corruptions ALT 2026