Tamara Broderick
Tamara Broderick
Associate Professor of EECS, Massachusetts Institute of Technology
Verified email at csail.mit.edu - Homepage
TitleCited byYear
Streaming variational bayes
T Broderick, N Boyd, A Wibisono, AC Wilson, MI Jordan
Advances in Neural Information Processing Systems, 1727-1735, 2013
Ellipticity of dark matter haloes with galaxy–galaxy weak lensing
R Mandelbaum, CM Hirata, T Broderick, U Seljak, J Brinkmann
Monthly Notices of the Royal Astronomical Society 370 (2), 1008-1024, 2006
MAD-Bayes: MAP-based asymptotic derivations from Bayes
T Broderick, B Kulis, M Jordan
International Conference on Machine Learning, 226-234, 2013
Beta processes, stick-breaking and power laws
T Broderick, MI Jordan, J Pitman
Bayesian analysis 7 (2), 439-476, 2012
Coresets for scalable Bayesian logistic regression
J Huggins, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4080-4088, 2016
Combinatorial clustering and the beta negative binomial process
T Broderick, L Mackey, J Paisley, MI Jordan
IEEE transactions on pattern analysis and machine intelligence 37 (2), 290-306, 2014
Faster solutions of the inverse pairwise Ising problem
T Broderick, M Dudik, G Tkacik, RE Schapire, W Bialek
arXiv preprint arXiv:0712.2437, 2007
Feature allocations, probability functions, and paintboxes
T Broderick, J Pitman, MI Jordan
Bayesian Analysis 8 (4), 801-836, 2013
Redshift accuracy requirements for future supernova and number count surveys
D Huterer, A Kim, LM Krauss, T Broderick
The Astrophysical Journal 615 (2), 595, 2004
Linear response methods for accurate covariance estimates from mean field variational Bayes
RJ Giordano, T Broderick, MI Jordan
Advances in Neural Information Processing Systems, 1441-1449, 2015
Real-time semiparametric regression
J Luts, T Broderick, MP Wand
Journal of Computational and Graphical Statistics 23 (3), 589-615, 2014
Cluster and feature modeling from combinatorial stochastic processes
T Broderick, MI Jordan, J Pitman
Statistical Science 28 (3), 289-312, 2013
Edge-exchangeable graphs and sparsity
D Cai, T Campbell, T Broderick
Advances in Neural Information Processing Systems, 4249-4257, 2016
Boosting variational inference
F Guo, X Wang, K Fan, T Broderick, DB Dunson
arXiv preprint arXiv:1611.05559, 2016
Optimistic concurrency control for distributed unsupervised learning
X Pan, JE Gonzalez, S Jegelka, T Broderick, MI Jordan
Advances in Neural Information Processing Systems, 1403-1411, 2013
Posteriors, conjugacy, and exponential families for completely random measures
T Broderick, AC Wilson, MI Jordan
Bernoulli 24 (4B), 3181-3221, 2018
Fast and flexible selection with a single switch
T Broderick, DJC MacKay
PloS one 4 (10), e7481, 2009
Bayesian coreset construction via greedy iterative geodesic ascent
T Campbell, T Broderick
arXiv preprint arXiv:1802.01737, 2018
Covariances, robustness and variational bayes
R Giordano, T Broderick, MI Jordan
The Journal of Machine Learning Research 19 (1), 1981-2029, 2018
Rapid, machine-learned resource allocation: application to high-redshift gamma-ray burst follow-up
AN Morgan, J Long, JW Richards, T Broderick, NR Butler, JS Bloom
The Astrophysical Journal 746 (2), 170, 2012
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