Stochastic Processes

A Contrastive Divergence for Combining Variational Inference and MCMC ICML 2019 Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization AAAI 2019 Adaptive Rao-Blackwellisation in Gibbs Sampling for Probabilistic Graphical Models AISTATS 2019 Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection AAAI 2019 A Family of Robust Stochastic Operators for Reinforcement Learning NeurIPS 2019 A General Framework for Multi-fidelity Bayesian Optimization with Gaussian Processes AISTATS 2019 A Generalization Bound for Online Variational Inference ACML 2019 A Generic Acceleration Framework for Stochastic Composite Optimization NeurIPS 2019 Amortized Monte Carlo Integration ICML 2019 Analysis and Synthesis of Vocal Flutter and Vocal Jitter Interspeech 2019 Approximate Bayesian Inference in Spatial Environments RSS 2019 Approximations of the Restless Bandit Problem JMLR 2019 AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs ICML 2019 ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks ICLR 2019 A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption ALT 2019 A Solvable High-Dimensional Model of GAN NeurIPS 2019 Assumed Density Filtering Q-learning IJCAI 2019 A Statistical Approach to Assessing Neural Network Robustness ICLR 2019 A Statistical Investigation of Long Memory in Language and Music ICML 2019 A Stochastic Composite Gradient Method with Incremental Variance Reduction NeurIPS 2019 Asynchronous Proximal Stochastic Gradient Algorithm for Composition Optimization Problems AAAI 2019 A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks ICML 2019 Autoregressive Policies for Continuous Control Deep Reinforcement Learning IJCAI 2019 Auxiliary Variational MCMC ICLR 2019 A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring CVPR 2019