Bayesian & Probabilistic

QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation NeurIPS 2024 QUITE: Quantifying Uncertainty in Natural Language Text in Bayesian Reasoning Scenarios EMNLP 2024 Random measure priors in Bayesian recovery from sketches JMLR 2024 R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning ICLR 2024 Response Time Improves Gaussian Process Models for Perception and Preferences UAI 2024 Rethinking and Extending the Probabilistic Inference Capacity of GNNs ICLR 2024 Revising Beliefs and Intentions in Stochastic Environments IJCAI 2024 Revisiting Kernel Attention with Correlated Gaussian Process Representation UAI 2024 Risk-Calibrated Human-Robot Interaction via Set-Valued Intent Prediction RSS 2024 Risk-sensitive control as inference with Rényi divergence NeurIPS 2024 Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024 Robust Gaussian Processes via Relevance Pursuit NeurIPS 2024 Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Prior ACML 2024 Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction AAAI 2024 Root Cause Explanation of Outliers under Noisy Mechanisms AAAI 2024 S$2$AC: Energy-Based Reinforcement Learning with Stein Soft Actor Critic ICLR 2024 Safe Exploitative Play with Untrusted Type Beliefs NeurIPS 2024 Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis AAAI 2024 Sample Average Approximation for Black-Box Variational Inference UAI 2024 Sample Efficient Bayesian Learning of Causal Graphs from Interventions NeurIPS 2024 Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs AISTATS 2024 Sample-Efficient Private Learning of Mixtures of Gaussians NeurIPS 2024 Sampling from the Mean-Field Stationary Distribution COLT 2024 Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier COLT 2024 Sampling-Resilient Multi-Object Tracking AAAI 2024