James Foulds
James Foulds
Bestätigte E-Mail-Adresse bei umbc.edu - Startseite
Zitiert von
Zitiert von
A review of multi-instance learning assumptions
JR Foulds, E Frank
Cambridge University Press 25 (1), 1-25, 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
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
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
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
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
F Dubois, R Mozul
11e colloque national en calcul des structures, 2013
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
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
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
Revisiting multiple-instance learning via embedded instance selection
J Foulds, E Frank
Australasian Joint Conference on Artificial Intelligence, 300-310, 2008
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
DP-EM: Differentially private expectation maximization
M Park, J Foulds, K Choudhary, M Welling
Artificial Intelligence and Statistics, 896-904, 2017
Learning instance weights in multi-instance learning
JR Foulds
The University of Waikato, 2008
Variational Bayes in private settings (VIPS)
M Park, J Foulds, K Chaudhuri, M Welling
arXiv preprint arXiv:1611.00340, 2016
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
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
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
Modeling scientific impact with topical influence regression
J Foulds, P Smyth
Proceedings of the 2013 Conference on Empirical Methods in Natural Language …, 2013
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
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