Michael I. Jordan
Michael I. Jordan
Professor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley
Verified email at - Homepage
Cited by
Cited by
Latent dirichlet allocation
DM Blei, AY Ng, MI Jordan
Journal of machine Learning research 3 (Jan), 993-1022, 2003
On spectral clustering: Analysis and an algorithm
A Ng, M Jordan, Y Weiss
Advances in neural information processing systems 14, 2001
Machine learning: Trends, perspectives, and prospects
MI Jordan, TM Mitchell
Science 349 (6245), 255-260, 2015
Trust region policy optimization
J Schulman, S Levine, P Abbeel, M Jordan, P Moritz
International conference on machine learning, 1889-1897, 2015
Adaptive mixtures of local experts
RA Jacobs, MI Jordan, SJ Nowlan, GE Hinton
Neural computation 3 (1), 79-87, 1991
Learning transferable features with deep adaptation networks
M Long, Y Cao, J Wang, M Jordan
International conference on machine learning, 97-105, 2015
Graphical models, exponential families, and variational inference
MJ Wainwright, MI Jordan
Foundations and Trends® in Machine Learning 1 (1–2), 1-305, 2008
Sharing clusters among related groups: Hierarchical Dirichlet processes
Y Teh, M Jordan, M Beal, D Blei
Advances in neural information processing systems 17, 2004
An introduction to variational methods for graphical models
MI Jordan, Z Ghahramani, TS Jaakkola, LK Saul
Machine learning 37, 183-233, 1999
An internal model for sensorimotor integration
DM Wolpert, Z Ghahramani, MI Jordan
Science 269 (5232), 1880-1882, 1995
Hierarchical mixtures of experts and the EM algorithm
MI Jordan, RA Jacobs
Neural computation 6 (2), 181-214, 1994
Distance metric learning with application to clustering with side-information
E Xing, M Jordan, SJ Russell, A Ng
Advances in neural information processing systems 15, 2002
High-dimensional continuous control using generalized advantage estimation
J Schulman, P Moritz, S Levine, M Jordan, P Abbeel
arXiv preprint arXiv:1506.02438, 2015
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
A Ng, MI Jordan
Advances in Neural Information Processing Systems 14, 841, 2002
An introduction to MCMC for machine learning
C Andrieu, N De Freitas, A Doucet, MI Jordan
Machine learning 50, 5-43, 2003
Optimal feedback control as a theory of motor coordination
E Todorov, MI Jordan
Nature neuroscience 5 (11), 1226-1235, 2002
Learning the kernel matrix with semidefinite programming
GRG Lanckriet, N Cristianini, P Bartlett, LE Ghaoui, MI Jordan
Journal of Machine learning research 5 (Jan), 27-72, 2004
Kalman filtering with intermittent observations
B Sinopoli, L Schenato, M Franceschetti, K Poolla, MI Jordan, SS Sastry
IEEE transactions on Automatic Control 49 (9), 1453-1464, 2004
Deep transfer learning with joint adaptation networks
M Long, H Zhu, J Wang, MI Jordan
International conference on machine learning, 2208-2217, 2017
Theoretically principled trade-off between robustness and accuracy
H Zhang, Y Yu, J Jiao, E Xing, L El Ghaoui, M Jordan
International conference on machine learning, 7472-7482, 2019
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