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Jonathan H. Huggins
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2020 – today
- 2023
- [c15]Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins:
A Targeted Accuracy Diagnostic for Variational Approximations. AISTATS 2023: 8351-8372 - [i19]Yu Wang, Mikolaj Kasprzak, Jonathan H. Huggins:
A Targeted Accuracy Diagnostic for Variational Approximations. CoRR abs/2302.12419 (2023) - [i18]Jonathan H. Huggins, Jeffrey W. Miller:
Reproducible Parameter Inference Using Bagged Posteriors. CoRR abs/2311.02019 (2023) - 2022
- [i17]Manushi Welandawe
, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
Robust, Automated, and Accurate Black-box Variational Inference. CoRR abs/2203.15945 (2022) - [i16]Jeffrey Negrea, Jun Yang, Haoyue Feng, Daniel M. Roy, Jonathan H. Huggins:
Statistical Inference with Stochastic Gradient Algorithms. CoRR abs/2207.12395 (2022) - 2021
- [c14]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: 7787-7798 - [i15]Akash Kumar Dhaka, Alejandro Catalina, Manushi Welandawe
, Michael Riis Andersen, Jonathan H. Huggins, Aki Vehtari:
Challenges and Opportunities in High-dimensional Variational Inference. CoRR abs/2103.01085 (2021) - 2020
- [c13]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Validated Variational Inference via Practical Posterior Error Bounds. AISTATS 2020: 1792-1802 - [c12]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. NeurIPS 2020 - [i14]Akash Kumar Dhaka, Alejandro Catalina, Michael Riis Andersen, Måns Magnusson, Jonathan H. Huggins, Aki Vehtari:
Robust, Accurate Stochastic Optimization for Variational Inference. CoRR abs/2009.00666 (2020)
2010 – 2019
- 2019
- [c11]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. AISTATS 2019: 796-805 - [c10]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. AISTATS 2019: 1822-1831 - [c9]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: 141-150 - [c8]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. ICML 2019: 6315-6324 - [i13]Raj Agrawal, Jonathan H. Huggins, Brian L. Trippe, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. CoRR abs/1905.06501 (2019) - [i12]Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. CoRR abs/1905.07499 (2019) - [i11]Jonathan H. Huggins, Mikolaj Kasprzak, Trevor Campbell, Tamara Broderick:
Practical Posterior Error Bounds from Variational Objectives. CoRR abs/1910.04102 (2019) - 2018
- [c7]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. NeurIPS 2018: 1903-1913 - [i10]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. CoRR abs/1806.07788 (2018) - [i9]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees. CoRR abs/1806.10234 (2018) - [i8]Jonathan H. Huggins, Trevor Campbell, Mikolaj Kasprzak, Tamara Broderick:
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach. CoRR abs/1809.09505 (2018) - [i7]Raj Agrawal, Trevor Campbell, Jonathan H. Huggins, Tamara Broderick:
Data-dependent compression of random features for large-scale kernel approximation. CoRR abs/1810.04249 (2018) - [i6]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 abs/1811.11790 (2018) - 2017
- [c6]Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick:
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS 2017: 3611-3621 - 2016
- [c5]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. NIPS 2016: 4080-4088 - [i5]Jonathan H. Huggins, Trevor Campbell, Tamara Broderick:
Coresets for Scalable Bayesian Logistic Regression. CoRR abs/1605.06423 (2016) - 2015
- [c4]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML 2015: 693-701 - [c3]Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners. ICML 2015: 1442-1451 - [i4]Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson:
Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models. CoRR abs/1501.00052 (2015) - [i3]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR abs/1503.00332 (2015) - [i2]Jonathan H. Huggins, Joshua B. Tenenbaum:
Risk and Regret of Hierarchical Bayesian Learners. CoRR abs/1505.04984 (2015) - 2014
- [c2]Jonathan H. Huggins, Cynthia Rudin:
Toward a Theory of Pattern Discovery. ISAIM 2014 - [c1]Jonathan H. Huggins, Cynthia Rudin:
A Statistical Learning Theory Framework for Supervised Pattern Discovery. SDM 2014: 506-514 - 2013
- [i1]Jonathan H. Huggins, Cynthia Rudin:
Toward a Theory of Pattern Discovery. CoRR abs/1307.0802 (2013)
Coauthor Index
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