Articles with public access mandates - Jonathan HugginsLearn more
Available somewhere: 17
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
Mandates: US National Science Foundation, US Department of Defense, Natural Sciences …
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
Mandates: Academy of Finland
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
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: Academy of Finland
Quantifying the accuracy of approximate diffusions and Markov chains
J Huggins, J Zou
Artificial Intelligence and Statistics, 382-391, 2017
Mandates: US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
Mandates: US Department of Defense
Reproducible model selection using bagged posteriors
JH Huggins, JW Miller
Bayesian analysis 18 (1), 79, 2022
Mandates: US National Science Foundation, US National Institutes of Health
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
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense, UK Engineering and …
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
Mandates: US National Institutes of Health
Robust, automated, and accurate black-box variational inference
M Welandawe, M Riis Anderson, A Vehtari, J Huggins
ArXiv, 2022
Mandates: US National Science Foundation, US National Institutes of Health, Academy of …
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
Mandates: US National Science Foundation, US Department of Defense
A framework for improving the reliability of black-box variational inference
M Welandawe, MR Andersen, A Vehtari, JH Huggins
Journal of Machine Learning Research 25 (219), 1-71, 2024
Mandates: Innovation Fund Denmark
A targeted accuracy diagnostic for variational approximations
Y Wang, M Kasprzak, JH Huggins
International Conference on Artificial Intelligence and Statistics, 8351-8372, 2023
Mandates: US National Science Foundation, US National Institutes of Health, European …
The feasibility of targeted test-trace-isolate for the control o f SARS-CoV-2 variants [version 1; peer review: 1 approved with
W Bradshaw, J Huggins, A Lloyd, K Esvelt
Mandates: Wellcome Trust
Reproducible parameter inference using bagged posteriors
JH Huggins, JW Miller
Electronic Journal of Statistics 18 (1), 1549-1585, 2024
Mandates: US National Science Foundation, US National Institutes of Health
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