Bayesian Inference

On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling JMLR 2024 On the Computational Complexity of Private High-dimensional Model Selection NeurIPS 2024 On the connection between Noise-Contrastive Estimation and Contrastive Divergence AISTATS 2024 On the True Distribution Approximation of Minimum Bayes-Risk Decoding NAACL 2024 On Truthing Issues in Supervised Classification JMLR 2024 On Unbiased Estimation for Partially Observed Diffusions JMLR 2024 Optimal Design for Human Preference Elicitation NeurIPS 2024 Optimal Exploration is no harder than Thompson Sampling AISTATS 2024 Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions JMLR 2024 Optimising Distributions with Natural Gradient Surrogates AISTATS 2024 Optimistic Information Directed Sampling COLT 2024 Outlier-robust Kalman Filtering through Generalised Bayes ICML 2024 Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models NAACL 2024 PAC-Bayes-Chernoff bounds for unbounded losses NeurIPS 2024 PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs AAAI 2024 PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models ICML 2024 Parallel Affine Transformation Tuning of Markov Chain Monte Carlo ICML 2024 Parallel-in-Time Probabilistic Numerical ODE Solvers JMLR 2024 Parameter Estimation in DAGs from Incomplete Data via Optimal Transport ICML 2024 Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization AAAI 2024 Partially Stochastic Infinitely Deep Bayesian Neural Networks ICML 2024 PASOA- PArticle baSed Bayesian Optimal Adaptive design ICML 2024 Path-Guided Particle-based Sampling ICML 2024 Pedestrian Attribute Recognition as Label-balanced Multi-label Learning ICML 2024 Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling JMLR 2024