Stochastic Methods

Hamiltonian Monte Carlo with Energy Conserving Subsampling JMLR 2019 High-Dimensional Optimization in Adaptive Random Subspaces NeurIPS 2019 Importance Sampling Policy Evaluation with an Estimated Behavior Policy ICML 2019 Imputing Missing Events in Continuous-Time Event Streams ICML 2019 Incorporating Behavioral Constraints in Online AI Systems AAAI 2019 Incremental Randomized Sketching for Online Kernel Learning ICML 2019 Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms AAAI 2019 Interpolated Spectral NGram Language Models ACL 2019 Joint Manifold Diffusion for Combining Predictions on Decoupled Observations CVPR 2019 Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number ICML 2019 KNG: The K-Norm Gradient Mechanism NeurIPS 2019 Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NeurIPS 2019 Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019 Local SGD Converges Fast and Communicates Little ICLR 2019 Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019 Metropolis-Hastings Generative Adversarial Networks ICML 2019 Minimum Stein Discrepancy Estimators NeurIPS 2019 Momentum-Based Variance Reduction in Non-Convex SGD NeurIPS 2019 Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems NeurIPS 2019 Nonconvex sampling with the Metropolis-adjusted Langevin algorithm COLT 2019 Off-Policy Policy Gradient with Stationary Distribution Correction UAI 2019 On First-Order Bounds, Variance and Gap-Dependent Bounds for Adversarial Bandits UAI 2019 Online High Rank Matrix Completion CVPR 2019 Online sampling from log-concave distributions NeurIPS 2019 Online Variance Reduction with Mixtures ICML 2019