Huggins, Jonathan

12 publications

NeurIPS 2021 Challenges and Opportunities in High Dimensional Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael R Andersen, Jonathan Huggins, Aki Vehtari

NeurIPS 2020 Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Michael R Andersen, Måns Magnusson, Jonathan Huggins, Aki Vehtari

AISTATS 2020 Validated Variational Inference via Practical Posterior Error Bounds Jonathan Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

AISTATS 2019 Data-Dependent Compression of Random Features for Large-Scale Kernel Approximation Raj Agrawal, Trevor Campbell, Jonathan Huggins, Tamara Broderick

ICML 2019 LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations Brian Trippe, Jonathan Huggins, Raj Agrawal, Tamara Broderick

ICML 2019 The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick

NeurIPS 2018 Random Feature Stein Discrepancies Jonathan Huggins, Lester Mackey

NeurIPS 2017 PASS-GLM: Polynomial Approximate Sufficient Statistics for Scalable Bayesian GLM Inference Jonathan Huggins, Ryan P. Adams, Tamara Broderick

AISTATS 2017 Quantifying the Accuracy of Approximate Diffusions and Markov Chains Jonathan Huggins, James Zou

NeurIPS 2016 Coresets for Scalable Bayesian Logistic Regression Jonathan Huggins, Trevor Campbell, Tamara Broderick

ICML 2015 JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes Jonathan Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

ICML 2015 Risk and Regret of Hierarchical Bayesian Learners Jonathan Huggins, Josh Tenenbaum