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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 Energy Decompositions for Partial Inference in GFlowNets ICLR 2024 Learning Equi-angular Representations for Online Continual Learning CVPR 2024 Learning Equilibria in Adversarial Team Markov Games: A Nonconvex-Hidden-Concave Min-Max Optimization Problem NeurIPS 2024 Learning Exceptional Subgroups by End-to-End Maximizing KL-Divergence ICML 2024 Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games AISTATS 2024 Learning Fair Policies for Multi-Stage Selection Problems from Observational Data AAAI 2024 Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer ICLR 2024 Learning flow functions of spiking systems L4DC 2024 Learning for CasADi: Data-driven Models in Numerical Optimization L4DC 2024 Learning, Forgetting, Remembering: Insights From Tracking LLM Memorization During Training EMNLP 2024 Learning for Transductive Threshold Calibration in Open-World Recognition CVPR 2024 Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions ICLR 2024 Learning from higher-order correlations, efficiently: hypothesis tests, random features, and neural networks NeurIPS 2024 Learning from Integral Losses in Physics Informed Neural Networks ICML 2024 Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation ICLR 2024 Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss IJCAI 2024 Learning from many trajectories JMLR 2024 Learning from Noisy Labels via Conditional Distributionally Robust Optimization NeurIPS 2024 Learning from Snapshots of Discrete and Continuous Data Streams NeurIPS 2024 Learning from Streaming Data when Users Choose ICML 2024 Learning Generalized Linear Programming Value Functions NeurIPS 2024 Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm NeurIPS 2024 Learning Hierarchical Polynomials with Three-Layer Neural Networks ICLR 2024 Learning Hypertrees From Shortest Path Queries ALT 2024 Learning in CubeRes Model Space for Anomaly Detection in 3D GPR Data IJCAI 2024