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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances ICML 2019
Anytime Online-to-Batch, Optimism and Acceleration ICML 2019
Acceleration of SVRG and Katyusha X by Inexact Preconditioning ICML 2019
Rao-Blackwellized Stochastic Gradients for Discrete Distributions ICML 2019
Imputing Missing Events in Continuous-Time Event Streams ICML 2019
Metropolis-Hastings Generative Adversarial Networks ICML 2019
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number ICML 2019
Importance Sampling Policy Evaluation with an Estimated Behavior Policy ICML 2019
Incorporating Behavioral Constraints in Online AI Systems AAAI 2019
Active Mini-Batch Sampling Using Repulsive Point Processes AAAI 2019
Interpolated Spectral NGram Language Models ACL 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems NIPS 2019
Stein Variational Gradient Descent With Matrix-Valued Kernels NIPS 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
KNG: The K-Norm Gradient Mechanism NIPS 2019
Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms AAAI 2019
Joint Manifold Diffusion for Combining Predictions on Decoupled Observations CVPR 2019
A Sufficient Condition for Convergences of Adam and RMSProp CVPR 2019
Optimal Stochastic and Online Learning with Individual Iterates NIPS 2019
Communication-efficient Distributed SGD with Sketching NIPS 2019
On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NIPS 2019
Selective Sampling-based Scalable Sparse Subspace Clustering NIPS 2019
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning NIPS 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NIPS 2019