Stochastic gradient hamiltonian monte carlo T Chen, E Fox, C Guestrin International conference on machine learning, 1683-1691, 2014 | 1099 | 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 | 616* | 2011 |
A complete recipe for stochastic gradient MCMC YA Ma, T Chen, E Fox Advances in neural information processing systems 28, 2015 | 584 | 2015 |
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 | 431 | 2019 |
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 (164), 1-20, 2021 | 413 | 2021 |
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 | 379 | 2008 |
Neural granger causality A Tank, I Covert, N Foti, A Shojaie, EB Fox IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (8), 4267-4279, 2021 | 343 | 2021 |
Nonparametric Bayesian learning of switching linear dynamical systems E Fox, E Sudderth, M Jordan, A Willsky Advances in neural information processing systems 21, 2008 | 298 | 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 | 289 | 2011 |
Granger causality: A review and recent advances A Shojaie, EB Fox Annual Review of Statistics and Its Application 9 (1), 289-319, 2022 | 263 | 2022 |
Sparse graphs using exchangeable random measures F Caron, EB Fox Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2017 | 261 | 2017 |
A bayesian approach for predicting the popularity of tweets T Zaman, EB Fox, ET Bradlow | 210 | 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 | 186 | 2009 |
Bayesian nonparametric learning of complex dynamical phenomena EB Fox Massachusetts Institute of Technology, 2009 | 169 | 2009 |
Advances in Neural Information Processing Systems, volume 32. Curran Associates E Fox, R Garnett Inc, 0 | 166 | |
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, 1281-1313, 2014 | 134 | 2014 |
Learning the parameters of determinantal point process kernels RH Affandi, E Fox, R Adams, B Taskar International Conference on Machine Learning, 1224-1232, 2014 | 126 | 2014 |
Control variates for stochastic gradient MCMC J Baker, P Fearnhead, EB Fox, C Nemeth Statistics and Computing 29, 599-615, 2019 | 124 | 2019 |
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 | 118 | 2010 |
Expectation-maximization for learning determinantal point processes JA Gillenwater, A Kulesza, E Fox, B Taskar Advances in Neural Information Processing Systems 27, 2014 | 110 | 2014 |