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Stochastic Methods

1,118 papers

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Papers

Stein Variational Gradient Descent With Matrix-Valued Kernels NIPS 2019
Gradient-based Adaptive Markov Chain Monte Carlo NIPS 2019
Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NIPS 2019
Communication-efficient Distributed SGD with Sketching NIPS 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NIPS 2019
SySCD: A System-Aware Parallel Coordinate Descent Algorithm NIPS 2019
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering NIPS 2019
Continuous-time Models for Stochastic Optimization Algorithms NIPS 2019
Selective Sampling-based Scalable Sparse Subspace Clustering NIPS 2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NIPS 2019
Tensor Monte Carlo: Particle Methods for the GPU era NIPS 2019
Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019
Minimum Stein Discrepancy Estimators NIPS 2019
Towards closing the gap between the theory and practice of SVRG 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
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning NIPS 2019
Exact sampling of determinantal point processes with sublinear time preprocessing NIPS 2019
KNG: The K-Norm Gradient Mechanism NIPS 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NIPS 2019