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Jonathan Huggins
Jonathan Huggins
Assistant Professor of Statistics, Boston University
Zweryfikowany adres z bu.edu - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Coresets for scalable Bayesian logistic regression
JH Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
2112016
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
1172021
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
48*2020
Random feature Stein discrepancies
J Huggins, L Mackey
Advances in Neural Information Processing Systems 31, 2018
452018
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
332014
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
322018
Robust inference and model criticism using bagged posteriors
JH Huggins, JW Miller
arXiv preprint arXiv:1912.07104, 2019
30*2019
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
29*2019
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
282017
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
272020
Quantifying the accuracy of approximate diffusions and Markov chains
J Huggins, J Zou
Artificial Intelligence and Statistics, 382-391, 2017
272017
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
232019
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
212019
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
202019
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
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
17*2014
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
162021
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
162019
Reproducible model selection using bagged posteriors
JH Huggins, JW Miller
Bayesian Analysis 18 (1), 79-104, 2023
12*2023
LR-GLM: High-dimensional Bayesian inference using low-rank data approximations
B Trippe, J Huggins, R Agrawal, T Broderick
International Conference on Machine Learning, 6315-6324, 2019
112019
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