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Jonathan Huggins
Jonathan Huggins
Assistant Professor of Statistics, Boston University
E-mailová adresa ověřena na: bu.edu - Domovská stránka
Název
Citace
Citace
Rok
Coresets for scalable Bayesian logistic regression
JH Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
2742016
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
1622021
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
68*2020
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
552021
Random feature Stein discrepancies
J Huggins, L Mackey
Advances in neural information processing systems 31, 2018
502018
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
402017
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
392020
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
392018
Robust inference and model criticism using bagged posteriors
JH Huggins, JW Miller
arXiv preprint arXiv:1912.07104, 2019
36*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
362014
Quantifying the accuracy of approximate diffusions and Markov chains
J Huggins, J Zou
Artificial Intelligence and Statistics, 382-391, 2017
342017
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
33*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
322019
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
312019
Reproducible model selection using bagged posteriors
JH Huggins, JW Miller
Bayesian analysis 18 (1), 79, 2022
28*2022
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
252019
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
202012
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
192019
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
162014
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
152021
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Články 1–20