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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)

Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm NeurIPS 2022 Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms JMLR 2022 Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective NeurIPS 2022 Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method JMLR 2022 Asymptotic optimality for active learning processes UAI 2022 Asymptotic Properties for Bayesian Neural Network in Besov Space NeurIPS 2022 Asymptotics of ℓ_2 Regularized Network Embeddings NeurIPS 2022 Asymptotics of smoothed Wasserstein distances in the small noise regime NeurIPS 2022 Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape JMLR 2022 AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract) AAAI 2022 Asynchronous Distributed Optimization with Stochastic Delays AISTATS 2022 Asynchronous Personalized Federated Learning with Irregular Clients ACML 2022 Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays NeurIPS 2022 Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits AISTATS 2022 A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model ICLR 2022 A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features ICLR 2022 A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization ICML 2022 A Theoretical Comparison of Graph Neural Network Extensions ICML 2022 A Theoretical Framework for Inference Learning NeurIPS 2022 A Theoretical Perspective on Hyperdimensional Computing (Extended Abstract) IJCAI 2022 A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning NeurIPS 2022 A Theoretical View on Sparsely Activated Networks NeurIPS 2022 A Theory of PAC Learnability under Transformation Invariances NeurIPS 2022 A theory of weight distribution-constrained learning NeurIPS 2022 A Tighter Analysis of Spectral Clustering, and Beyond ICML 2022