| Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 646 | 2014 |
| 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 | 510* | 2011 |
| A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in neural information processing systems 28, 2015 | 376 | 2015 |
| 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 | 334 | 2008 |
| 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 | 244 | 2011 |
| Nonparametric Bayesian learning of switching linear dynamical systems E Fox, E Sudderth, M Jordan, A Willsky Advances in neural information processing systems 21, 2008 | 241 | 2008 |
| Sparse graphs using exchangeable random measures F Caron, EB Fox Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2017 | 198 | 2017 |
| Curran Associates H Wallach, H Larochelle, A Beygelzimer, F d’Alché-Buc, E Fox, R Garnett Inc.: Red Hook, NY, USA 32, 8024-8035, 2019 | 186 | 2019 |
| A bayesian approach for predicting the popularity of tweets T Zaman, EB Fox, ET Bradlow The Annals of Applied Statistics 8 (3), 1583-1611, 2014 | 176 | 2014 |
| 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 | 170 | 2009 |
| Bayesian nonparametric learning of complex dynamical phenomena EB Fox Massachusetts Institute of Technology, 2009 | 153 | 2009 |
| Improving reproducibility in machine learning research: a report from the NeurIPS 2019 reproducibility program J Pineau, P Vincent-Lamarre, K Sinha, V Larivière, A Beygelzimer, ... Journal of Machine Learning Research 22, 2021 | 106 | 2021 |
| Joint modeling of multiple time series via the beta process with application to motion capture segmentation EB Fox, MC Hughes, EB Sudderth, MI Jordan The Annals of Applied Statistics 8 (3), 1281-1313, 2014 | 103 | 2014 |
| 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 | 103 | 2010 |
| Learning the parameters of determinantal point process kernels RH Affandi, E Fox, R Adams, B Taskar International Conference on Machine Learning, 1224-1232, 2014 | 101 | 2014 |
| Neural granger causality A Tank, I Covert, N Foti, A Shojaie, E Fox arXiv preprint arXiv:1802.05842, 2018 | 96 | 2018 |
| Stochastic variational inference for hidden Markov models N Foti, J Xu, D Laird, E Fox Advances in neural information processing systems 27, 2014 | 86 | 2014 |
| Stochastic variational inference for hidden Markov models N Foti, J Xu, D Laird, E Fox Advances in neural information processing systems 27, 2014 | 86 | 2014 |
| Expectation-maximization for learning determinantal point processes JA Gillenwater, A Kulesza, E Fox, B Taskar Advances in Neural Information Processing Systems 27, 2014 | 84 | 2014 |
| Control variates for stochastic gradient MCMC J Baker, P Fearnhead, EB Fox, C Nemeth Statistics and Computing 29 (3), 599-615, 2019 | 78 | 2019 |
