Jonathan Huggins

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	All	Since 2019
Citations	1099	942
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Tamara BroderickAssociate Professor of EECS, Massachusetts Institute of TechnologyVerified email at mit.edu
Trevor CampbellAssociate Professor, Statistics, UBCVerified email at stat.ubc.ca
Kevin EsveltAssociate Professor, MIT Media LabVerified email at mit.edu
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
Mikolaj KasprzakDepartment of Information Systems, Decision Sciences and Statistics, ESSEC Business SchoolVerified email at essec.edu
Alun LloydDrexel Professor of Mathematics, North Carolina State UniversityVerified email at ncsu.edu
Michael Riis AndersenAssociate professor, Technical University of DenmarkVerified email at dtu.dk
Aki VehtariProfessor, Aalto UniversityVerified email at aalto.fi
Jeffrey W MillerHarvard T.H. Chan School of Public HealthVerified email at hsph.harvard.edu
Manushi WelandaweBoston University Mathematics and StatisticsVerified email at bu.edu
Ethan AlleyMIT Media LabVerified email at mit.edu
Lester MackeyMicrosoft ResearchVerified email at microsoft.com
Akash Kumar DhakaDoctoral Student, Aalto UniversityVerified email at aalto.fi
Alejandro CatalinaAalto UniversityVerified email at aalto.fi
Daniel M. RoyResearch Director, Vector Institute; Prof., U. Toronto (Statistics, CS)Verified email at utoronto.ca
Brian L TrippeAssistant Professor of Statistics, Stanford UniversityVerified email at stanford.edu
Eftychios A. PnevmatikakisMeta Reality LabsVerified email at flatironinstitute.org
James ZouStanford UniversityVerified email at stanford.edu
Jonathan P. HowRichard C. Maclaurin Professor of Aerospace Engineering, Massachusetts Institute of TechnologyVerified email at mit.edu
Ardavan SaeediMassachusetts Institute of TechnologyVerified email at mit.edu

Jonathan Huggins

Assistant Professor of Statistics, Boston University

Verified email at bu.edu - Homepage

Machine Learning Data Science Uncertainty Quantification Bayesian Inference


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Coresets for scalable Bayesian logistic regression JH Huggins, T Campbell, T Broderick Advances in Neural Information Processing Systems, 4080-4088, 2016	259	2016
Bidirectional contact tracing could dramatically improve COVID-19 control WJ Bradshaw, EC Alley, JH Huggins, AL Lloyd, KM Esvelt Nature communications 12 (1), 232, 2021	156	2021
Validated variational inference via practical posterior error bounds J Huggins, M Kasprzak, T Campbell, T Broderick International Conference on Artificial Intelligence and Statistics, 1792-1802, 2020	63*	2020
Random feature Stein discrepancies J Huggins, L Mackey Advances in neural information processing systems 31, 2018	47	2018
Challenges and opportunities in high dimensional variational inference AK Dhaka, A Catalina, M Welandawe, MR Andersen, J Huggins, A Vehtari Advances in Neural Information Processing Systems 34, 7787-7798, 2021	43	2021
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach JH Huggins, T Campbell, M Kasprzak, T Broderick arXiv preprint arXiv:1809.09505, 2018	39	2018
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference J Huggins, RP Adams, T Broderick Advances in Neural Information Processing Systems 30, 2017	38	2017
Robust, accurate stochastic optimization for variational inference AK Dhaka, A Catalina, MR Andersen, M Magnusson, J Huggins, A Vehtari Advances in Neural Information Processing Systems 33, 10961-10973, 2020	36	2020
Robust inference and model criticism using bagged posteriors JH Huggins, JW Miller arXiv preprint arXiv:1912.07104, 2019	35*	2019
Fast Kalman filtering and forward–backward smoothing via a low-rank perturbative approach EA Pnevmatikakis, KR Rad, J Huggins, L Paninski Journal of Computational and Graphical Statistics 23 (2), 316-339, 2014	35	2014
Sequential Monte Carlo as Approximate Sampling: bounds, adaptive resampling via -ESS, and an application to Particle Gibbs JH Huggins, DM Roy Bernoulli 25 (1), 584–622, 2019	34*	2019
Quantifying the accuracy of approximate diffusions and Markov chains J Huggins, J Zou Artificial Intelligence and Statistics, 382-391, 2017	32	2017
Truncated random measures T Campbell, JH Huggins, JP How, T Broderick	29	2019
The kernel interaction trick: Fast Bayesian discovery of pairwise interactions in high dimensions R Agrawal, B Trippe, J Huggins, T Broderick International Conference on Machine Learning, 141-150, 2019	28	2019
Reproducible model selection using bagged posteriors JH Huggins, JW Miller Bayesian analysis 18 (1), 79, 2023	24*	2023
Data-dependent compression of random features for large-scale kernel approximation R Agrawal, T Campbell, J Huggins, T Broderick The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	24	2019
Fast state-space methods for inferring dendritic synaptic connectivity A Pakman, J Huggins, C Smith, L Paninski Journal of computational neuroscience 36, 415-443, 2014	19*	2014
Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime JH Huggins, L Paninski Journal of Computational Neuroscience 32, 347-366, 2012	19*	2012
Scalable Gaussian process inference with finite-data mean and variance guarantees JH Huggins, T Campbell, M Kasprzak, T Broderick The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	18	2019
The mutational signature comprehensive analysis toolkit (musicatk) for the discovery, prediction, and exploration of mutational signatures A Chevalier, S Yang, Z Khurshid, N Sahelijo, T Tong, JH Huggins, ... Cancer research 81 (23), 5813-5817, 2021	16	2021

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