Stochastic Processes

A Multi-Channel Neural Graphical Event Model with Negative Evidence AAAI 2020 A Multi-Hypothesis Approach to Color Constancy CVPR 2020 Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement JMLR 2020 An Improved Analysis of Stochastic Gradient Descent with Momentum NeurIPS 2020 Approximate Inference with Wasserstein Gradient Flows AISTATS 2020 AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation ICML 2020 A Statistical Framework for Low-bitwidth Training of Deep Neural Networks NeurIPS 2020 Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms JMLR 2020 A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound AAAI 2020 A Theoretical Case Study of Structured Variational Inference for Community Detection AISTATS 2020 BackPACK: Packing more into Backprop ICLR 2020 Bandit Samplers for Training Graph Neural Networks NeurIPS 2020 Bandits for BMO Functions ICML 2020 Batched Coarse Ranking in Multi-Armed Bandits NeurIPS 2020 Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation UAI 2020 Batch Stationary Distribution Estimation ICML 2020 Bayesian Deep Ensembles via the Neural Tangent Kernel NeurIPS 2020 Bayesian Probabilistic Numerical Integration with Tree-Based Models NeurIPS 2020 Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy AAAI 2020 Black-Box Inference for Non-Linear Latent Force Models AISTATS 2020 Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics ICML 2020 Bootstrapping neural processes NeurIPS 2020 Calibration, Entropy Rates, and Memory in Language Models ICML 2020 Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study NeurIPS 2020 Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics AAAI 2020