posterior contraction

Universal priors: solving empirical Bayes via Bayesian inference and pretraining COLT 2026 Bayesian Sparse Gaussian Mixture Model for Clustering in High Dimensions JMLR 2025 Frequentist Guarantees of Distributed (Non)-Bayesian Inference JMLR 2025 Nonparametric Regression on Random Geometric Graphs Sampled from Submanifolds JMLR 2025 Gaussian Processes with Errors in Variables: Theory and Computation JMLR 2023 On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances ICML 2023 Posterior Contraction for Deep Gaussian Process Priors JMLR 2023 Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2023 The Art of BART: Minimax Optimality over Nonhomogeneous Smoothness in High Dimension JMLR 2023 Variational Gaussian processes for linear inverse problems NeurIPS 2023 Double Spike Dirichlet Priors for Structured Weighting JMLR 2022 Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models JMLR 2022 On the inability of Gaussian process regression to optimally learn compositional functions NeurIPS 2022 Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures JMLR 2022 Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective JMLR 2022 Dangers of Bayesian Model Averaging under Covariate Shift NeurIPS 2021 Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee NeurIPS 2020 Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees NeurIPS 2015 Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis ICML 2014