Jonathan H. Huggins

According to our database, Jonathan H. Huggins authored at least 24 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Framework for Improving the Reliability of Black-box Variational Inference.

Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

J. Mach. Learn. Res., 2024

Tuning-free coreset Markov chain Monte Carlo.

Naitong Chen, Jonathan H. Huggins, Trevor Campbell

CoRR, 2024

2023

Reproducible Parameter Inference Using Bagged Posteriors.

Jonathan H. Huggins, Jeffrey W. Miller

CoRR, 2023

A Targeted Accuracy Diagnostic for Variational Approximations.

Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Statistical Inference with Stochastic Gradient Algorithms.

Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins

CoRR, 2022

Robust, Automated, and Accurate Black-box Variational Inference.

Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins

CoRR, 2022

2021

Challenges and Opportunities in High Dimensional Variational Inference.

Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Robust, Accurate Stochastic Optimization for Variational Inference.

Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Validated Variational Inference via Practical Posterior Error Bounds.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Practical Posterior Error Bounds from Variational Objectives.

Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick

CoRR, 2019

LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations.

Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions.

Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 36th International Conference on Machine Learning, 2019

Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Data-dependent compression of random features for large-scale kernel approximation.

Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data.

Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick

CoRR, 2018

Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach.

Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick

CoRR, 2018

Random Feature Stein Discrepancies.

Jonathan H. Huggins, Lester Mackey

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference.

Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Coresets for Scalable Bayesian Logistic Regression.

Jonathan H. Huggins, Trevor Campbell, Tamara Broderick

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models.

Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson

CoRR, 2015

Risk and Regret of Hierarchical Bayesian Learners.

Jonathan H. Huggins, Joshua B. Tenenbaum

Proceedings of the 32nd International Conference on Machine Learning, 2015

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes.

Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A Statistical Learning Theory Framework for Supervised Pattern Discovery.

Jonathan H. Huggins, Cynthia Rudin

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Toward a Theory of Pattern Discovery.

Jonathan H. Huggins, Cynthia Rudin

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2014