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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions ICML 2021
Bias-Robust Bayesian Optimization via Dueling Bandits ICML 2021
DivAug: Plug-In Automated Data Augmentation With Explicit Diversity Maximization ICCV 2021
Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information NIPS 2021
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NIPS 2021
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems ICML 2021
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits NIPS 2021
Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021
MetaGrad: Adaptation using Multiple Learning Rates in Online Learning JMLR 2021
From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021
Parametric Graph for Unimodal Ranking Bandit ICML 2021
A unified view of likelihood ratio and reparameterization gradients AISTATS 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates AISTATS 2021
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC AISTATS 2021
Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021
Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels NIPS 2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity NIPS 2021
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NIPS 2021
Communication-efficient SGD: From Local SGD to One-Shot Averaging NIPS 2021
Optimal Algorithms for Stochastic Contextual Preference Bandits NIPS 2021
Generalization Guarantee of SGD for Pairwise Learning NIPS 2021
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning NIPS 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NIPS 2021
Scalable Thompson Sampling using Sparse Gaussian Process Models NIPS 2021