Bayesian Learning

VariBAD: Variational Bayes-Adaptive Deep RL via Meta-Learning JMLR 2021 Vector Quantized Bayesian Neural Network Inference for Data Streams AAAI 2021 What Are Bayesian Neural Network Posteriors Really Like? ICML 2021 A Bayesian Nonparametrics View into Deep Representations NeurIPS 2020 Algorithmic recourse under imperfect causal knowledge: a probabilistic approach NeurIPS 2020 A Locally Adaptive Bayesian Cubature Method AISTATS 2020 A Multi-Hypothesis Approach to Color Constancy CVPR 2020 Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring NeurIPS 2020 A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning ICLR 2020 An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles IJCAI 2020 Asynchronous Gibbs Sampling AISTATS 2020 A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments AISTATS 2020 BaKer-Nets: Bayesian Random Kernel Mapping Networks IJCAI 2020 Bandit Algorithms Based on Thompson Sampling for Bounded Reward Distributions ALT 2020 Baxter Permutation Process NeurIPS 2020 Bayesian Adversarial Human Motion Synthesis CVPR 2020 Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract) IJCAI 2020 Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks NeurIPS 2020 Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods NeurIPS 2020 Bayesian Graph Neural Networks with Adaptive Connection Sampling ICML 2020 Bayesian Hierarchical Words Representation Learning ACL 2020 Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020 Bayesian Learning with Adaptive Load Allocation Strategies L4DC 2020 Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels NeurIPS 2020 Bayesian Meta Sampling for Fast Uncertainty Adaptation ICLR 2020