James Foulds

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Lise GetoorProfessor of Computer Science, University of California, Santa CruzVerified email at ucsc.edu
Kamrun Naher KeyaPh.D. Student, UMBCVerified email at umbc.edu
Rashidul IslamPh.D. Candidate, University of Maryland, Baltimore CountyVerified email at umbc.edu
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
Padhraic SmythProfessor, Computer Science, University of California IrvineVerified email at ics.uci.edu
Eibe FrankProfessor, Department of Computer Science, University of WaikatoVerified email at cs.waikato.ac.nz
Kamalika ChaudhuriAssociate Professor of Computer Science, UCSD.Verified email at eng.ucsd.edu
Mijung ParkUniversity of British ColumbiaVerified email at cs.ubc.ca
Shobeir FakhraeiSenior Machine Learning Scientist, AmazonVerified email at amazon.com
Se Jin SongUniversity of California San DiegoVerified email at ucsd.edu
Richard BonneauNew York UniversityVerified email at nyu.edu
Yoshiki Vázquez-BaezaBiomeSenseVerified email at biomesense.com
Alexander A. AksenovUniversity of ConnecticutVerified email at uconn.edu
Robert A QuinnAssistant Professor, Department of Biochemistry and Molecular Biology, Michigan State University,Verified email at msu.edu
Pieter C DorresteinUCSDVerified email at ucsd.edu
James T. MortonEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentVerified email at nih.gov
Christopher DuBoisAppleVerified email at apple.com
Bert HuangAssistant Professor, Tufts University Department of Computer ScienceVerified email at vt.edu
Marilyn WalkerProfessor of Computer Science and Engineering, University of California Santa CruzVerified email at ucsc.edu
Yannis PapanikolaouPhD Aristotle University of ThessalonikiVerified email at csd.auth.gr

James Foulds

University of Maryland, Baltimore County

Verified email at umbc.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A review of multi-instance learning assumptions J Foulds, E Frank The knowledge engineering review 25 (1), 1-25, 2010	358	2010
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation J Foulds, L Boyles, C DuBois, P Smyth, M Welling Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013	137	2013
Joint models of disagreement and stance in online debate D Sridhar, J Foulds, B Huang, L Getoor, M Walker Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015	135	2015
Collective spammer detection in evolving multi-relational social networks S Fakhraei, J Foulds, M Shashanka, L Getoor Proceedings of the 21th acm sigkdd international conference on knowledge …, 2015	121	2015
Learning representations of microbe–metabolite interactions JT Morton, AA Aksenov, LF Nothias, JR Foulds, RA Quinn, MH Badri, ... Nature methods 16 (12), 1306-1314, 2019	113	2019
Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems P Kouki, S Fakhraei, J Foulds, M Eirinaki, L Getoor Proceedings of the 9th ACM Conference on Recommender Systems, 99-106, 2015	111	2015
HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades X He, T Rekatsinas, J Foulds, L Getoor, Y Liu ICML, 2015	107	2015
An intersectional definition of fairness JR Foulds, R Islam, KN Keya, S Pan 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1918-1921, 2020	73	2020
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis J Foulds, J Geumlek, M Welling, K Chaudhuri Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence …, 2016	72	2016
Weakly supervised models of aspect-sentiment for online course discussion forums A Ramesh, SH Kumar, J Foulds, L Getoor Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015	72	2015
A dynamic relational infinite feature model for longitudinal social networks J Foulds, C DuBois, A Asuncion, C Butts, P Smyth Proceedings of the fourteenth international conference on artificial …, 2011	66	2011
DP-EM: Differentially private expectation maximization M Park, J Foulds, K Choudhary, M Welling Artificial Intelligence and Statistics, 896-904, 2017	44	2017
Dense distributions from sparse samples: improved gibbs sampling parameter estimators for LDA Y Papanikolaou, JR Foulds, TN Rubin, G Tsoumakas The Journal of Machine Learning Research 18 (1), 2058-2115, 2017	34	2017
Revisiting multiple-instance learning via embedded instance selection J Foulds, E Frank Australasian Joint Conference on Artificial Intelligence, 300-310, 2008	34	2008
Latent Topic Networks: A Versatile Probabilistic Programming Framework for Topic Models J Foulds, SH Kumar, L Getoor Proceedings of The 32nd International Conference on Machine Learning, 777-786, 2015	29	2015
Variational bayes in private settings (vips) M Park, J Foulds, K Chaudhuri, M Welling arXiv preprint arXiv:1611.00340, 2016	27	2016
Learning instance weights in multi-instance learning JR Foulds The University of Waikato, 2008	26	2008
Bayesian Modeling of Intersectional Fairness: The Variance of Bias∗ JR Foulds, R Islam, KN Keya, S Pan Proceedings of the 2020 SIAM International Conference on Data Mining, 424-432, 2020	21	2020
Multi-instance mixture models and semi-supervised learning J Foulds, P Smyth Proceedings of the 2011 SIAM International Conference on Data Mining, 606-617, 2011	20	2011
Modeling Scientific Impact with Topical Influence Regression. JR Foulds, P Smyth EMNLP, 113-123, 2013	17	2013

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