bayesian regression

Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood AAAI 2025 Bayesian Regression Markets JMLR 2024 Learning Causal Markov Boundaries with Mixed Observational and Experimental Data PGM 2024 Measuring acoustic dissimilarity of hierarchical markers in task-oriented dialogue with MFCC-based dynamic time warping Interspeech 2024 Factorial SDE for Multi-Output Gaussian Process Regression AISTATS 2023 Posterior Consistency for Bayesian Relevance Vector Machines JMLR 2023 Fourier Representations for Black-Box Optimization over Categorical Variables AAAI 2022 On the inability of Gaussian process regression to optimally learn compositional functions NeurIPS 2022 Robust Bayesian Regression via Hard Thresholding NeurIPS 2022 Safe Active Learning for Multi-Output Gaussian Processes AISTATS 2022 Sparse Additive Gaussian Process Regression JMLR 2022 Spatial Multivariate Trees for Big Data Bayesian Regression JMLR 2022 Bayesian Topic Regression for Causal Inference EMNLP 2021 Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models JMLR 2020 Deep Structured Mixtures of Gaussian Processes AISTATS 2020 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 Bayesian Tensor Regression JMLR 2017 Linearly constrained Gaussian processes NeurIPS 2017 Multiresolution Kernel Approximation for Gaussian Process Regression NeurIPS 2017 Multi-way Interacting Regression via Factorization Machines NeurIPS 2017 Student-t Process Regression with Student-t Likelihood IJCAI 2017 Probing the Compositionality of Intuitive Functions NeurIPS 2016 Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews ACML 2016 A Marginalized Particle Gaussian Process Regression NeurIPS 2012 Learning curves for multi-task Gaussian process regression NeurIPS 2012