Stochastic Processes

Rotting bandits are no harder than stochastic ones AISTATS 2019 RUDDER: Return Decomposition for Delayed Rewards NeurIPS 2019 Sample Adaptive MCMC NeurIPS 2019 Sample Efficient Graph-Based Optimization with Noisy Observations AISTATS 2019 Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics AISTATS 2019 Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers AISTATS 2019 Scalable Collapsed Inference for High-Dimensional Topic Models NAACL 2019 Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NeurIPS 2019 Scalable Training of Inference Networks for Gaussian-Process Models ICML 2019 Semi-Cyclic Stochastic Gradient Descent ICML 2019 Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds ALT 2019 Sequential Neural Processes NeurIPS 2019 Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases AAAI 2019 Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions ICML 2019 STCN: Stochastic Temporal Convolutional Networks ICLR 2019 Stein Point Markov Chain Monte Carlo ICML 2019 Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement ICML 2019 Stochastic Canonical Correlation Analysis JMLR 2019 Stochastic Gradient Descent Learns State Equations with Nonlinear Activations COLT 2019 Stochastic Gradient Push for Distributed Deep Learning ICML 2019 Stochastic Gradient Trees ACML 2019 Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations JMLR 2019 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NeurIPS 2019 Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization ICML 2019 Switching Linear Dynamics for Variational Bayes Filtering ICML 2019