bayesian nonparametrics

Covariate-dependent Hierarchical Dirichlet Processes JMLR 2026 Degree of Interference: A General Framework For Causal Inference Under Interference JMLR 2025 Dirichlet Process-Based Robust Clustering Using the Median-of-Means Estimator IJCAI 2025 Locally Private Causal Inference for Randomized Experiments JMLR 2025 Weighted Sum of Gaussian Process Latent Variable Models AISTATS 2025 Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization NeurIPS 2024 Flexible Bayesian Product Mixture Models for Vector Autoregressions JMLR 2024 Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data JMLR 2024 Random measure priors in Bayesian recovery from sketches JMLR 2024 Spatial properties of Bayesian unsupervised trees COLT 2024 Warped Diffusion for Latent Differentiation Inference AISTATS 2024 A VAE for Transformers with Nonparametric Variational Information Bottleneck ICLR 2023 Bayesian Federated Neural Matching That Completes Full Information AAAI 2023 Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability NeurIPS 2023 Bayesian Spiked Laplacian Graphs JMLR 2023 EEGNN: Edge Enhanced Graph Neural Network with a Bayesian Nonparametric Graph Model AISTATS 2023 Gaussian Processes with Errors in Variables: Theory and Computation JMLR 2023 Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations JMLR 2023 Learning-augmented count-min sketches via Bayesian nonparametrics JMLR 2023 Nearest Neighbor Dirichlet Mixtures 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 Sequential Monte Carlo Learning for Time Series Structure Discovery ICML 2023 Sparse Spectral Bayesian Permanental Process with Generalized Kernel AISTATS 2023 Weibull Racing Survival Analysis with Competing Events, Left Truncation, and Time-Varying Covariates JMLR 2023