Jonathan Huggins
Jonathan Huggins
Assistant Professor of Statistics, Boston University
Email verificata su - Home page
Citata da
Citata da
Coresets for scalable Bayesian logistic regression
J Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
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
Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime
JH Huggins, L Paninski
Journal of Computational Neuroscience 32 (2), 347-366, 2012
Quantifying the accuracy of approximate diffusions and Markov chains
JH Huggins, J Zou
arXiv preprint arXiv:1605.06420, 2016
Sequential Monte Carlo as Approximate Sampling: bounds, adaptive resampling via -ESS, and an application to Particle Gibbs
JH Huggins, DM Roy
arXiv preprint arXiv:1503.00966, 2015
Fast state-space methods for inferring dendritic synaptic connectivity
A Pakman, J Huggins, C Smith, L Paninski
Journal of computational neuroscience 36 (3), 415-443, 2014
Infinite structured hidden semi-Markov models
JH Huggins, F Wood
arXiv preprint arXiv:1407.0044, 2014
Random feature stein discrepancies
J Huggins, L Mackey
Advances in Neural Information Processing Systems, 1899-1909, 2018
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
J Huggins, RP Adams, T Broderick
Advances in Neural Information Processing Systems, 3611-3621, 2017
Truncated random measures
T Campbell, JH Huggins, JP How, T Broderick
Bernoulli 25 (2), 1256-1288, 2019
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
Scalable Gaussian process inference with finite-data mean and variance guarantees
JH Huggins, T Campbell, M Kasprzak, T Broderick
arXiv preprint arXiv:1806.10234, 2018
Risk and regret of hierarchical bayesian learners
JH Huggins, JB Tenenbaum
arXiv preprint arXiv:1505.04984, 2015
Jump-means: Small-variance asymptotics for Markov jump processes
JH Huggins, K Narasimhan, A Saeedi, VK Mansinghka
arXiv preprint arXiv:1503.00332, 2015
Data-dependent compression of random features for large-scale kernel approximation
R Agrawal, T Campbell, JH Huggins, T Broderick
arXiv preprint arXiv:1810.04249, 2018
Fast robustness quantification with variational Bayes
R Giordano, T Broderick, R Meager, J Huggins, M Jordan
arXiv preprint arXiv:1606.07153, 2016
A simple feature-copying approach for long-distance dependencies
M Vilain, J Huggins, B Wellner
Proceedings of the Thirteenth Conference on Computational Natural Language …, 2009
A statistical learning theory framework for supervised pattern discovery
JH Huggins, C Rudin
Proceedings of the 2014 SIAM International Conference on Data Mining, 506-514, 2014
Practical Posterior Error Bounds from Variational Objectives
JH Huggins, M Kasprzak, T Campbell, T Broderick
arXiv preprint arXiv:1910.04102, 2019
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
R Agrawal, JH Huggins, B Trippe, T Broderick
arXiv preprint arXiv:1905.06501, 2019
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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