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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Reducing the variance in online optimization by transporting past gradients NIPS 2019
Understanding the Role of Momentum in Stochastic Gradient Methods NIPS 2019
SpiderBoost and Momentum: Faster Variance Reduction Algorithms NIPS 2019
High-Dimensional Optimization in Adaptive Random Subspaces NIPS 2019
Gradient-based Adaptive Markov Chain Monte Carlo NIPS 2019
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering NIPS 2019
Minimum Stein Discrepancy Estimators NIPS 2019
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning NIPS 2019
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory NIPS 2019
Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning NIPS 2019
Pseudo-Extended Markov chain Monte Carlo NIPS 2019
Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples NIPS 2019
The Randomized Midpoint Method for Log-Concave Sampling NIPS 2019
Risk-Averse Stochastic Convex Bandit AISTATS 2019
Unifying Orthogonal Monte Carlo Methods ICML 2019
Optimal Mini-Batch and Step Sizes for SAGA ICML 2019
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019
Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation NIPS 2018
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization NIPS 2018
The Lingering of Gradients: How to Reuse Gradients Over Time NIPS 2018
Training Deep Models Faster with Robust, Approximate Importance Sampling NIPS 2018
Learning with SGD and Random Features NIPS 2018
$D^2$: Decentralized Training over Decentralized Data ICML 2018
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization NIPS 2018
Variance-Reduced Stochastic Gradient Descent on Streaming Data NIPS 2018