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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

No-regret learning with high-probability in adversarial Markov decision processes UAI 2021
Efficient debiased evidence estimation by multilevel Monte Carlo sampling UAI 2021
Variance reduction in frequency estimators via control variates method UAI 2021
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference UAI 2021
Stochastic Anderson Mixing for Nonconvex Stochastic Optimization NIPS 2021
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NIPS 2021
Combinatorial Blocking Bandits with Stochastic Delays ICML 2021
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems AISTATS 2021
Stochastic Bandits with Linear Constraints AISTATS 2021
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC AISTATS 2021
Explicit Regularization of Stochastic Gradient Methods through Duality AISTATS 2021
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes AISTATS 2021
No-Regret Reinforcement Learning with Heavy-Tailed Rewards AISTATS 2021
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo AISTATS 2021
Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates AISTATS 2021
A unified view of likelihood ratio and reparameterization gradients AISTATS 2021
Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization JMLR 2021
A Stochastic Newton Algorithm for Distributed Convex Optimization NIPS 2021
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits NIPS 2021
Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021
Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021
Stochastic Online Optimization using Kalman Recursion JMLR 2021
Consensus-Based Optimization on the Sphere: Convergence to Global Minimizers and Machine Learning JMLR 2021
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals JMLR 2021
SNIPS: Solving Noisy Inverse Problems Stochastically NIPS 2021