Jonathan H. Huggins

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 31

Top Topics

Statistical Learning Theory

High Dimensional

Gaussian Process

Variational Inference

Top Venues

Publications

Jonathan H. Huggins, Jeffrey W. Miller
Reproducible Parameter Inference Using Bagged Posteriors. CoRR (2023)
Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins
A Targeted Accuracy Diagnostic for Variational Approximations. CoRR (2023)
Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins
A Targeted Accuracy Diagnostic for Variational Approximations. AISTATS (2023)
Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
Robust, Automated, and Accurate Black-box Variational Inference. CoRR (2022)
Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins
Statistical Inference with Stochastic Gradient Algorithms. CoRR (2022)
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
Challenges and Opportunities in High-dimensional Variational Inference. CoRR (2021)
Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS (2021)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR (2020)
Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS (2020)
Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS (2020)
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML (2019)
Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR (2019)
Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick
Practical Posterior Error Bounds from Variational Objectives. CoRR (2019)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS (2019)
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
Data-dependent compression of random features for large-scale kernel approximation. AISTATS (2019)
Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML (2019)
Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR (2019)
Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
Data-dependent compression of random features for large-scale kernel approximation. CoRR (2018)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR (2018)
Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR (2018)
Jonathan H. Huggins, Lester Mackey
Random Feature Stein Discrepancies. NeurIPS (2018)
Jonathan H. Huggins, Lester Mackey
Random Feature Stein Discrepancies. CoRR (2018)
Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR (2018)
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS (2017)
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
Coresets for Scalable Bayesian Logistic Regression. NIPS (2016)
Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
Coresets for Scalable Bayesian Logistic Regression. CoRR (2016)
Jonathan H. Huggins, Joshua B. Tenenbaum
Risk and Regret of Hierarchical Bayesian Learners. CoRR (2015)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML (2015)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR (2015)
Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR (2015)
Jonathan H. Huggins, Joshua B. Tenenbaum
Risk and Regret of Hierarchical Bayesian Learners. ICML (2015)
Jonathan H. Huggins, Cynthia Rudin
Toward a Theory of Pattern Discovery. ISAIM (2014)
Jonathan H. Huggins, Cynthia Rudin
A Statistical Learning Theory Framework for Supervised Pattern Discovery. SDM (2014)
Jonathan H. Huggins, Cynthia Rudin
Toward a Theory of Pattern Discovery. CoRR (2013)