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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
'5
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'25

Papers (including subtopics)

Variance Control for Distributional Reinforcement Learning ICML 2023 Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency COLT 2023 Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement Interspeech 2023 Variance-Reduced Conservative Policy Iteration ALT 2023 Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies NeurIPS 2023 Variance Reduced Online Gradient Descent for Kernelized Pairwise Learning with Limited Memory ACML 2023 Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top ICLR 2023 Variational Autoencoding Neural Operators ICML 2023 Variational Boosted Soft Trees AISTATS 2023 Variational Gaussian processes for linear inverse problems NeurIPS 2023 Variational Gaussian Processes with Decoupled Conditionals NeurIPS 2023 Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data JMLR 2023 Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing NeurIPS 2023 Variational Inference for Deblending Crowded Starfields JMLR 2023 Variational Inference for Neyman-Scott Processes AISTATS 2023 Variational Inference with Gaussian Score Matching NeurIPS 2023 Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations JMLR 2023 Variational Open-Domain Question Answering ICML 2023 Variational Sparse Inverse Cholesky Approximation for Latent Gaussian Processes via Double Kullback-Leibler Minimization ICML 2023 Variational Wasserstein Barycenters with C-cyclical Monotonicity Regularization AAAI 2023 VaRT: Variational Regression Trees NeurIPS 2023 VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback JMLR 2023 VeCare: Statistical Acoustic Sensing for Automotive In-Cabin Monitoring NSDI 2023 Vector Optimization with Stochastic Bandit Feedback AISTATS 2023 Vector-Valued Control Variates ICML 2023