Bayesian Learning

Bayesian Methods for Semi-supervised Text Annotation COLING 2020 Bayesian Multi-type Mean Field Multi-agent Imitation Learning NeurIPS 2020 Bayesian network structure learning with causal effects in the presence of latent variables PGM 2020 Bayesian Optimization of Risk Measures NeurIPS 2020 Bayesian Reinforcement Learning via Deep, Sparse Sampling AISTATS 2020 Bayesian Robust Optimization for Imitation Learning NeurIPS 2020 Bayesian Sparsification of Deep C-valued Networks ICML 2020 BayesRace: Learning to race autonomously using prior experience CoRL 2020 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020 Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties NeurIPS 2020 BINOCULARS for efficient, nonmyopic sequential experimental design ICML 2020 Capsule Routing via Variational Bayes AAAI 2020 Causal Inference using Gaussian Processes with Structured Latent Confounders ICML 2020 Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics AAAI 2020 Constraing-Based Learning for Continous-Time Bayesian Networks PGM 2020 Continuous Time Bayesian Networks with Clocks ICML 2020 Convergence Rates of Gradient Descent and MM Algorithms for Bradley-Terry Models AISTATS 2020 Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning ICLR 2020 Deep active inference agents using Monte-Carlo methods NeurIPS 2020 Distributionally Robust Bayesian Optimization AISTATS 2020 Efficient Low Rank Gaussian Variational Inference for Neural Networks NeurIPS 2020 Ensemble Gaussian Processes with Spectral Features for Online Interactive Learning with Scalability AISTATS 2020 Ensembling geophysical models with Bayesian Neural Networks NeurIPS 2020 Event-Driven Continuous Time Bayesian Networks AAAI 2020 Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data JMLR 2020