Emily B. Fox
Title
Cited by
Cited by
Year
Stochastic gradient hamiltonian monte carlo
T Chen, E Fox, C Guestrin
International conference on machine learning, 1683-1691, 2014
4882014
A sticky HDP-HMM with application to speaker diarization
EB Fox, EB Sudderth, MI Jordan, AS Willsky
The Annals of Applied Statistics, 1020-1056, 2011
471*2011
An HDP-HMM for systems with state persistence
EB Fox, EB Sudderth, MI Jordan, AS Willsky
Proceedings of the 25th international conference on Machine learning, 312-319, 2008
3182008
A complete recipe for stochastic gradient MCMC
YA Ma, T Chen, EB Fox
arXiv preprint arXiv:1506.04696, 2015
2972015
Bayesian nonparametric inference of switching dynamic linear models
E Fox, EB Sudderth, MI Jordan, AS Willsky
IEEE Transactions on Signal Processing 59 (4), 1569-1585, 2011
2142011
Nonparametric Bayesian learning of switching linear dynamical systems
E Fox, E Sudderth, M Jordan, A Willsky
Advances in Neural Information Processing Systems 21, 457-464, 2008
2122008
Sparse graphs using exchangeable random measures
F Caron, EB Fox
Journal of the Royal Statistical Society. Series B, Statistical Methodology …, 2017
1632017
Sharing features among dynamical systems with beta processes
EB Fox, EB Sudderth, MI Jordan, AS Willsky
Advances in Neural Information Processing Systems, 549-557, 2009
1622009
A bayesian approach for predicting the popularity of tweets
T Zaman, EB Fox, ET Bradlow
Annals of Applied Statistics 8 (3), 1583-1611, 2014
1542014
Bayesian nonparametric learning of complex dynamical phenomena
EB Fox
Massachusetts Institute of Technology, 2009
1422009
Nonparametric Bayesian learning of switching linear dynamical systems
EB Fox, EB Sudderth, MI Jordan, AS Willsky
Advances in Neural Information Processing Systems, 457-464, 2009
1062009
Bayesian nonparametric methods for learning Markov switching processes
EB Fox, EB Sudderth, MI Jordan, AS Willsky
IEEE Signal Processing Magazine 27 (6), 43-54, 2010
952010
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
EB Fox, MC Hughes, EB Sudderth, MI Jordan
Annals of Applied Statistics 8 (3), 1281-1313, 2014
942014
Learning the parameters of determinantal point process kernels
RH Affandi, E Fox, R Adams, B Taskar
International Conference on Machine Learning, 1224-1232, 2014
832014
Expectation-maximization for learning determinantal point processes
J Gillenwater, A Kulesza, E Fox, B Taskar
arXiv preprint arXiv:1411.1088, 2014
792014
Stochastic variational inference for hidden Markov models
NJ Foti, J Xu, D Laird, EB Fox
arXiv preprint arXiv:1411.1670, 2014
682014
Stochastic variational inference for hidden Markov models
NJ Foti, J Xu, D Laird, EB Fox
arXiv preprint arXiv:1411.1670, 2014
682014
Control variates for stochastic gradient MCMC
J Baker, P Fearnhead, EB Fox, C Nemeth
Statistics and Computing 29 (3), 599-615, 2019
622019
Hierarchical Dirichlet processes for tracking maneuvering targets
EB Fox, EB Sudderth, AS Willsky
2007 10th international conference on information fusion, 1-8, 2007
602007
Approximate inference in continuous determinantal point processes
RH Affandi, EB Fox, B Taskar
arXiv preprint arXiv:1311.2971, 2013
532013
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Articles 1–20