Jonathan H. Huggins

38 papers A* 7A 6Misc 1Journal 22Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms. CoRR Xiaoyu Wang, Mikolaj J. Kasprzak, Jeffrey Negrea, Solesne Bourguin, Jonathan H. Huggins
2025	A	`conf`	Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG. UAI Naitong Chen, Jonathan H. Huggins, Trevor Campbell
2024	J	`jnl`	A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2024	J	`jnl`	Tuning-free coreset Markov chain Monte Carlo. CoRR Naitong Chen, Jonathan H. Huggins, Trevor Campbell
2023	A	`conf`	A Targeted Accuracy Diagnostic for Variational Approximations. AISTATS Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins
2023	J	`jnl`	A Targeted Accuracy Diagnostic for Variational Approximations. CoRR Yu Wang, Mikolaj J. Kasprzak, Jonathan H. Huggins
2023	J	`jnl`	Reproducible Parameter Inference Using Bagged Posteriors. CoRR Jonathan H. Huggins, Jeffrey W. Miller
2022	J	`jnl`	Robust, Automated, and Accurate Black-box Variational Inference. CoRR Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins
2022	J	`jnl`	Statistical Inference with Stochastic Gradient Algorithms. CoRR Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins
2021	A*	`conf`	Challenges and Opportunities in High Dimensional Variational Inference. NeurIPS Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2021	J	`jnl`	Challenges and Opportunities in High-dimensional Variational Inference. CoRR Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari
2020	A*	`conf`	Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020	J	`jnl`	Robust, Accurate Stochastic Optimization for Variational Inference. CoRR Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari
2020	A	`conf`	Validated Variational Inference via Practical Posterior Error Bounds. AISTATS Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	A	`conf`	Data-dependent compression of random features for large-scale kernel approximation. AISTATS Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2019	A*	`conf`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	J	`jnl`	LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick
2019	J	`jnl`	Practical Posterior Error Bounds from Variational Objectives. CoRR Jonathan H. Huggins, Mikolaj J. Kasprzak, Trevor Campbell, Tamara Broderick
2019	A	`conf`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2019	A*	`conf`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. ICML Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick
2019	J	`jnl`	The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick
2018	J	`jnl`	Data-dependent compression of random features for large-scale kernel approximation. CoRR Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick
2018	J	`jnl`	Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2018	A*	`conf`	Random Feature Stein Discrepancies. NeurIPS Jonathan H. Huggins, Lester Mackey
2018	J	`jnl`	Random Feature Stein Discrepancies. CoRR Jonathan H. Huggins, Lester Mackey
2018	J	`jnl`	Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. CoRR Miriam Shiffman, William T. Stephenson, Geoffrey Schiebinger, Jonathan H. Huggins, Trevor Campbell, Aviv Regev, Tamara Broderick
2018	J	`jnl`	Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR Jonathan H. Huggins, Trevor Campbell, Mikolaj J. Kasprzak, Tamara Broderick
2017	—	`conf`	PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
2016	—	`conf`	Coresets for Scalable Bayesian Logistic Regression. NIPS Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2016	J	`jnl`	Coresets for Scalable Bayesian Logistic Regression. CoRR Jonathan H. Huggins, Trevor Campbell, Tamara Broderick
2015	J	`jnl`	Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson
2015	A*	`conf`	JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka
2015	J	`jnl`	JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
2015	A*	`conf`	Risk and Regret of Hierarchical Bayesian Learners. ICML Jonathan H. Huggins, Joshua B. Tenenbaum
2015	J	`jnl`	Risk and Regret of Hierarchical Bayesian Learners. CoRR Jonathan H. Huggins, Joshua B. Tenenbaum
2014	A	`conf`	A Statistical Learning Theory Framework for Supervised Pattern Discovery. SDM Jonathan H. Huggins, Cynthia Rudin
2014	Misc	`conf`	Toward a Theory of Pattern Discovery. ISAIM Jonathan H. Huggins, Cynthia Rudin
2013	J	`jnl`	Toward a Theory of Pattern Discovery. CoRR Jonathan H. Huggins, Cynthia Rudin