default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJonathan H. Huggins
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
[j1]Manushi Welandawe, Michael Riis Andersen, Aki Vehtari, Jonathan H. Huggins:
A Framework for Improving the Reliability of Black-box Variational Inference. J. Mach. Learn. Res. 25: 219:1-219:71 (2024)- [i20]Naitong Chen, Jonathan H. Huggins, Trevor Campbell:
Tuning-free coreset Markov chain Monte Carlo. CoRR abs/2410.18973 (2024) - 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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-09 13:04 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
