| A review of multi-instance learning assumptions J Foulds, E Frank The knowledge engineering review 25 (1), 1-25, 2010 | 493 | 2010 |
| 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 | 265 | 2019 |
| 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 | 255 | 2020 |
| 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 | 168 | 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 | 164 | 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 | 160 | 2013 |
| 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 | 150 | 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 | 125 | 2015 |
| 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 | 124 | 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 | 88 | 2015 |
| 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 | 82 | 2021 |
| Mitigating demographic Bias in AI-based resume filtering KV Deshpande, S Pan, JR Foulds Adjunct publication of the 28th ACM conference on user modeling, adaptation …, 2020 | 76 | 2020 |
| 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 | 75 | 2011 |
| 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 | 61 | 2020 |
| DP-EM: Differentially private expectation maximization M Park, J Foulds, K Choudhary, M Welling Artificial Intelligence and Statistics, 896-904, 2017 | 61 | 2017 |
| Data mining: practical machine learning tools and techniques (Ian H.) Witten Morgan Kaufmann, 2011 | 55 | 2011 |
| Fair representation learning for heterogeneous information networks Z Zeng, R Islam, KN Keya, J Foulds, Y Song, S Pan Proceedings of the International AAAI Conference on Web and Social Media 15 …, 2021 | 49 | 2021 |
| Comparison of patients’ and staff’s perspectives on the causes of violence and aggression in psychiatric inpatient settings: An integrative review A Fletcher, M Crowe, J Manuel, J Foulds Journal of Psychiatric and Mental Health Nursing 28 (5), 924-939, 2021 | 48 | 2021 |
| Dense distributions from sparse samples: improved Gibbs sampling parameter estimators for LDA Y Papanikolaou, JR Foulds, TN Rubin, G Tsoumakas Journal of Machine Learning Research 18 (62), 1-58, 2017 | 47 | 2017 |
| Trapping llm hallucinations using tagged context prompts P Feldman, JR Foulds, S Pan arXiv preprint arXiv:2306.06085, 2023 | 38 | 2023 |
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