Bayesian Inference

Rethinking Variational Inference for Probabilistic Programs with Stochastic Support NeurIPS 2022 Revisiting Active Sets for Gaussian Process Decoders NeurIPS 2022 Revisiting the Effects of Stochasticity for Hamiltonian Samplers ICML 2022 RL with KL penalties is better viewed as Bayesian inference EMNLP 2022 Robust and Accurate Superquadric Recovery: A Probabilistic Approach CVPR 2022 Robust Anytime Learning of Markov Decision Processes NeurIPS 2022 Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap AISTATS 2022 Robust Bayesian Regression via Hard Thresholding NeurIPS 2022 Robust Deep Learning from Crowds with Belief Propagation AISTATS 2022 Robust Depth Completion with Uncertainty-Driven Loss Functions AAAI 2022 Robust expected information gain for optimal Bayesian experimental design using ambiguity sets UAI 2022 Robust learning of tractable probabilistic models UAI 2022 Robust Multi-Objective Bayesian Optimization Under Input Noise ICML 2022 Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits IJCAI 2022 Robust Neural Posterior Estimation and Statistical Model Criticism NeurIPS 2022 Robust Probabilistic Time Series Forecasting AISTATS 2022 Safe Active Learning for Multi-Output Gaussian Processes AISTATS 2022 Sample-Then-Optimize Batch Neural Thompson Sampling NeurIPS 2022 Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022 Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translation EMNLP 2022 Sampling from Log-Concave Distributions with Infinity-Distance Guarantees NeurIPS 2022 Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data AISTATS 2022 Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space NeurIPS 2022 Scalable Bayesian Network Structure Learning with Splines PGM 2022 Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation ICML 2022