Coresets for scalable Bayesian logistic regression JH Huggins, T Campbell, T Broderick Advances in Neural Information Processing Systems, 4080-4088, 2016 | 251 | 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 | 149 | 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 | 57* | 2020 |
Random feature Stein discrepancies J Huggins, L Mackey Advances in neural information processing systems 31, 2018 | 46 | 2018 |
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 | 38 | 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 | 36 | 2021 |
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 | 36 | 2017 |
Robust inference and model criticism using bagged posteriors JH Huggins, JW Miller arXiv preprint arXiv:1912.07104, 2019 | 34* | 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 | 34 | 2014 |
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 | 33 | 2020 |
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 |
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 | 28 | 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 | 25 | 2019 |
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 | 23 | 2019 |
Reproducible model selection using bagged posteriors JH Huggins, JW Miller Bayesian analysis 18 (1), 79, 2023 | 22* | 2023 |
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 | 17 | 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 | 14 | 2021 |