Bayesian Inference

On the Convergence of the Shapley Value in Parametric Bayesian Learning Games ICML 2022 On the detrimental effect of invariances in the likelihood for variational inference NeurIPS 2022 On the Finite-Time Performance of the Knowledge Gradient Algorithm ICML 2022 On the Importance of Gradient Norm in PAC-Bayesian Bounds NeurIPS 2022 On the Practicality of Deterministic Epistemic Uncertainty ICML 2022 On the Robustness to Misspecification of α-posteriors and Their Variational Approximations JMLR 2022 On the Value of Prior in Online Learning to Rank AISTATS 2022 On Tracking Dialogue State by Inheriting Slot Values in Mentioned Slot Pools IJCAI 2022 On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization AISTATS 2022 On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification NeurIPS 2022 Optimal Scaling for Locally Balanced Proposals in Discrete Spaces NeurIPS 2022 Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees NeurIPS 2022 Optimizing Sequential Experimental Design with Deep Reinforcement Learning ICML 2022 Orbital MCMC AISTATS 2022 Out of Distribution Data Detection Using Dropout Bayesian Neural Networks AAAI 2022 Out of Distribution Detection via Neural Network Anchoring ACML 2022 PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization NeurIPS 2022 PAC-Bayesian domain adaptation bounds for multiclass learners UAI 2022 PAC-Bayes Information Bottleneck ICLR 2022 PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime AISTATS 2022 Parallel MCMC Without Embarrassing Failures AISTATS 2022 Parallel Tempering With a Variational Reference NeurIPS 2022 Parameterized Completeness Results for Bayesian Inference PGM 2022 Pareto Set Learning for Expensive Multi-Objective Optimization NeurIPS 2022 Partial likelihood Thompson sampling UAI 2022