James Foulds
James Foulds
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A review of multi-instance learning assumptions
J Foulds, E Frank
The knowledge engineering review 25 (1), 1-25, 2010
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
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
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
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
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
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
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
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
DP-EM: Differentially private expectation maximization
M Park, J Foulds, K Choudhary, M Welling
Artificial Intelligence and Statistics, 896-904, 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
Revisiting multiple-instance learning via embedded instance selection
J Foulds, E Frank
Australasian Joint Conference on Artificial Intelligence, 300-310, 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
Variational bayes in private settings (vips)
M Park, J Foulds, K Chaudhuri, M Welling
arXiv preprint arXiv:1611.00340, 2016
Learning instance weights in multi-instance learning
JR Foulds
The University of Waikato, 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
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
Debiasing career recommendations with neural fair collaborative filtering
R Islam, KN Keya, Z Zeng, S Pan, J Foulds
Proceedings of the Web Conference 2021, 3779-3790, 2021
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