Bayesian Learning

Self-Correcting Bayesian Optimization through Bayesian Active Learning NeurIPS 2023 Sharp Calibrated Gaussian Processes NeurIPS 2023 Sparse Bayesian optimization AISTATS 2023 Sparse Spectral Bayesian Permanental Process with Generalized Kernel AISTATS 2023 Spectral Bayesian Uncertainty for Image Super-Resolution CVPR 2023 Stochastic Approximation Algorithms for Systems of Interacting Particles NeurIPS 2023 Stochastic Tree Ensembles for Estimating Heterogeneous Effects AISTATS 2023 Surveillance Evasion Through Bayesian Reinforcement Learning AISTATS 2023 Survival Permanental Processes for Survival Analysis with Time-Varying Covariates NeurIPS 2023 The Bayesian Learning Rule JMLR 2023 The Bayesian Stability Zoo NeurIPS 2023 The Behavior and Convergence of Local Bayesian Optimization NeurIPS 2023 The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data NeurIPS 2023 Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023 Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NeurIPS 2023 Transport Reversible Jump Proposals AISTATS 2023 Unexpected Improvements to Expected Improvement for Bayesian Optimization NeurIPS 2023 Variational Gaussian Processes with Decoupled Conditionals NeurIPS 2023 Variational Inference for Deblending Crowded Starfields JMLR 2023 Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss Information ACL 2023 Accelerated Linearized Laplace Approximation for Bayesian Deep Learning NeurIPS 2022 Accurate and Scalable Gaussian Processes for Fine-Grained Air Quality Inference AAAI 2022 Active Bayesian Causal Inference NeurIPS 2022 Active Structure Learning of Bayesian Networks in an Observational Setting JMLR 2022 Adapting the Linearised Laplace Model Evidence for Modern Deep Learning ICML 2022