Publications (see also my Google Scholar page)

This is very out of date. For more recent publications, please see my Google Scholar.

2021

R. Islam, K.N. Keya, Z. Zeng, S. Pan, and J.R. Foulds. Debiasing career recommendations with neural fair collaborative filtering. In Proceedings of the Web Conference (WWW) (accepted, in press), 2021.

[PDF]

K.N. Keya, R. Islam, S. Pan, I. Stockwell, and J.R. Foulds. Equitable allocation of healthcare resources with fair survival models. In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) (accepted, in press). SIAM, 2021.

Z. Zeng, R. Islam, K. Keya, J. Foulds, Y. Song, and S. Pan. Fair Heterogeneous Network Embeddings. Proceedings of the 15th International AAAI Conference on Web and Social Media (ICWSM) (accepted, in press), 2021.

2020

J.R. Foulds and S. Pan. Are parity-based notions of AI fairness desirable? Bulletin of the IEEE Technical Committee on Data Engineering (DEB), 43(4):51–73, 2020.

[PDF}

G. Shan, J. Foulds, and S. Pan. Causal Feature Selection with Dimension Reduction for Interpretable Text Classification. ArXiv Preprint arXiv:2010.04609v1 [cs.LG], 2020.

[ArXiv preprint]

R. Islam, K. Keya, Z. Zeng, S. Pan, and J. Foulds. Neural Fair Collaborative Filtering. ArXiv preprint arXiv:2009.08955v1 [cs.IR], 2020.

[ArXiv preprint]

K. Keya, R. Islam, S. Pan, I. Stockwell and J. R. Foulds. Equitable Allocation of Healthcare Resources with Fair Cox Models. AAAI Fall Symposium on AI in Government and Public Sector (AAAI FSS-20), 2020.

[PDF]

K. Deshpande, S. Pan and J. R. Foulds. Mitigating Demographic Bias in AI-based Resume Filtering. Fairness in User Modeling, Adaptation and Personalization (FairUMAP), 2020.

[PDF]

[Talk Video]

M. Park, J. R. Foulds, K. Chaudhuri, and M. Welling. Variational Bayes in Private Settings (VIPS). Journal of Artificial Intelligence Research (JAIR) 68:109-157, 2020.

[PDF]

[Arxiv preprint]

J. R. Foulds, M. Park, K. Chaudhuri, and M. Welling. Variational Bayes in Private Settings (VIPS) (Extended Abstract). 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI) Journal Track (accepted, in press), 2020.

[PDF]

[5 minute talk video]

[15 minute poster talk]

[Poster]

C. Wang, K. Wang, A. Bian, R. Islam, K. Keya, J. R. Foulds and S. Pan. A User Study on a De-biased Career Recommender System. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2020.

[PDF]

M. Rahman and J. R. Foulds. End-to-End Joint Modeling for Fake News Detection. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2020.

[PDF]

K. Deshpande, S. Pan and J. R. Foulds. Mitigating Socio-lingustic Bias in Job Recommendation. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2020.

[PDF]

J. R. Foulds, R. Islam, K. Keya, S. Pan. Bayesian Modeling of Intersectional Fairness: The Variance of Bias. SIAM International Conference on Data Mining (SDM), ArXiv preprint arXiv:1811.07255 [cs.LG], 2020.

[PDF]

[Supplementary]

[Arxiv preprint]

J. R. Foulds, R. Islam, K. Keya, and S. Pan. An Intersectional Definition of Fairness. 36th IEEE International Conference on Data Engineering (ICDE). ArXiv preprint arXiv:1807.08362 [CS.LG], 2020.

[PDF]

[Arxiv long version]

[Code: DF metric implemented in the IBM Research AI Fairness 360 toolkit]

[AIF 360 Github page]

[Slides]

[Talk Video]

2019

J. T. Morton, A. Aksenov, L.-F. Nothias-Scaglia, J. R. Foulds, R. A. Quinn, M. H. Badri, T. L. Swenson, M. W. Van Goethem, T. R. Northen, Y. Vasquez-Beaza, M. Wang, N. A. Bokulich, A. Watters, S.-J. Song, R. Bonneau, P. C. Dorrestein, and R. Knight. Learning representations of microbe-metabolite interactions. Nature Methods, 2019. [Article]
J. R. Foulds, R. Islam, K. Keya, and S. Pan. Differential fairness. NeurIPS 2019 Workshop on Machine Learning with Guarantees, 2019.
[PDF] [Arxiv long version] [Poster] [Code: DF metric implemented in the IBM Research AI Fairness 360 toolkit] [AIF 360 Github page]

K. Keya, Y. Papanikolaou, and J. R. Foulds. Neural embedding allocation: Distributed representations of topic models. ArXiv preprint arXiv:1909.04702 [cs.CL], 2019.

[Arxiv preprint]

R. Islam, K. Keya, S. Pan, and J. R. Foulds. Mitigating demographic biases in social media-based recommender systems. The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Social Impact Track (extended abstract), 2019.

[PDF]

N. Pathak, J. R. Foulds, N. Roy, N. Banerjee, R. Robucci. A Bayesian Data Analytics Approach to Buildings' Thermal Parameter Estimation. ACM e-Energy, 2019.

[PDF]

[ACM digital library]

[Arxiv preprint]

[Bibtex]

R. Islam and J. R. Foulds. Scalable collapsed inference for high-dimensional topic models. Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019.

[PDF]

[Bibtex]

2018

T. Ghiwaa and J. R. Foulds. Training WGANs with peer instruction. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2018.

[PDF]

R. Islam and J. R. Foulds. Towards a highly efficient online inference algorithm for latent Dirichlet allocation. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2018.

[PDF]

K. Keya, Y. Papanikolaou, and J. R. Foulds. Neural embedding allocation: Distributed representations of words, topics, and documents. Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL), 2018.

[PDF]

J. R. Foulds. Mixed Membership Word Embeddings for Computational Social Science. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.

[PDF]

[Supplementary]

[Arxiv preprint]

[Bibtex]

[Code]

[Slides]

[Poster]

2017

Y. Papanikolaou, J. R. Foulds, T. N. Rubin, G. Tsoumakas. Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA. Journal of Machine Learning Research (JMLR), 18(62):1-58, 2017.

[PDF]

[Arxiv preprint]

[Bibtex]

[Code]

M. Park, J. R. Foulds, K. Chaudhuri, M. Welling. DP-EM: Differentially Private Expectation Maximization. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

[PDF]

[Supplementary]

[Bibtex]

2016

J. R. Foulds, J. Geumlek, M. Welling, and K. Chaudhuri. On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis. Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI), 2016.

[PDF]

[Slides]

[Poster]

[ArXiv preprint]

[Bibtex]

J. R. Foulds. Mixed Membership Word Embeddings: Corpus-Specific Embeddings Without Big Data. SoCal Machine Learning Symposium (SCMLS), 2016.

Winner of the runner-up prize for best presentation!

[PDF]

[Slides]

[Poster]

M. Park, J. R. Foulds, K. Chaudhuri, M. Welling. Private Topic Modeling. NIPS Workshop on Private Multi-Party Machine Learning (PMPML), 2016.

[Arxiv preprint arXiv:1609.04120 [stat.ML]

2015

A. Grycner, G. Weikum, J. Pujara, J. R. Foulds, and L. Getoor. RELLY: Inferring hypernym relationships between relational phrases. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015.

[PDF]

[Bibtex]

[Data]

P. Kouki, S. Fakhraei, J. R. Foulds, M. Eirinaki, and L. Getoor. HyPER: A flexible and extensible probabilistic framework for hybrid recommender systems. Proceedings of the 9th ACM Conference on Recommender Systems (RecSys), 2015.

[PDF]

[Slides]

[Talk]

S. Fakhraei, J. R. Foulds, M. Shashanka, and L. Getoor. Collective spammer detection in evolving multi-relational social networks. Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015.

[PDF]

[Slides]

[Poster]

J. R. Foulds, S. H. Kumar, and L. Getoor. Latent topic networks: A versatile probabilistic programming framework for topic models. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.

[PDF]

[Slides]

[Supplementary]

[Code is available with the

PSL

X. He, T. Rekatsinas, J. R. Foulds, L. Getoor, and Y. Liu. HawkesTopic: A joint model for network inference and topic modeling from text-based cascades. Proceedings of the 32nd International Conference on Machine Learning (ICML), 2015.

[PDF]

[Supplementary]

A. Ramesh, S. H. Kumar, J. R. Foulds, and L. Getoor. Weakly supervised models of aspect-sentiment for online course discussion forums. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.

[PDF]

[Slides]

[Bibtex]

D. Sridhar, J. R. Foulds, B. Huang, M. Walker, and L. Getoor. Joint models of disagreement and stance in online debate. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.

[PDF]

[Slides]

[Bibtex]

2014

J. R. Foulds, P. Smyth. Annealing paths for the evaluation of topic models. Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI), 2014.
[PDF] [Slides] [Poster] [Bibtex] [Code]

A. Grycner, G. Weikum, J. Pujara, J. Foulds, L. Getoor.A unified probabilistic approach for semantic clustering of relational phrases. 4th Workshop on Automated Knowledge Base Construction (AKBC), 2014.

[PDF]

D. Sridhar, J. R. Foulds, B. Huang, M. Walker, L. Getoor. Collective classification of stance and disagreement in online debate forums. Bay Area Machine Learning Symposium (BayLearn), 2014.
One of only six submissions which were accepted for oral presentation.
[PDF]

2013

J. R. Foulds, P. Smyth. Modeling scientific impact with topical influence regression. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
[PDF] [Slides] [Bibtex]

J. R. Foulds, L. Boyles, C. DuBois, P. Smyth and M. Welling. Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013.
[PDF] [Slides] [Poster] [Julia code (fast)] [Matlab code (easy to understand)] [Bibtex]
The long version, including convergence proofs, is available on the arXiv .
The SCVB0 algorithm is implemented in MeTA. Other c++ implementations are available here and here.
See also the slides for a longer presentation, given at eBay Research Labs.

J. R. Foulds and P. Smyth. Robust evaluation of topic models. In NIPS Workshop on Topic Models, 2013.

[PDF]

[Poster]

J. R. Foulds and D. Görür. Diverse personalization with determinantal point process eigenmixtures. In NIPS Workshop on Personalization, 2013.

[PDF]

[Poster, which includes additional results]

2012

J. R. Foulds and P. Smyth. Modeling scientific impact with topical influence regression. In NIPS Workshop on Algorithmic and Statistical Approaches for Large Social Network Data Sets, 2012.

2011

C. DuBois, J. R. Foulds, P. Smyth. Latent set models for two-mode network data. Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM), 2011.

[PDF]

J. R. Foulds, A. Asuncion, C. DuBois, C. T. Butts, P. Smyth. A dynamic relational infinite feature model for longitudinal social networks. Proceedings of the 14th International Conference on AI and Statistics (AISTATS), April 2011.

[PDF]

[Code]

J. R. Foulds, N. Navaroli, P. Smyth, A. Ihler. Revisiting MAP estimation, message passing and perfect graphs. Proceedings of the 14th International Conference on AI and Statistics (AISTATS), April 2011.

[PDF]

J. R. Foulds and P. Smyth. Multi-instance mixture models and semi-supervised learning. SIAM International Conference on Data Mining (SDM), April 2011.

[PDF]

2010 and earlier

J. R. Foulds and E. Frank. Speeding up and boosting diverse density learning. In Proc 13th International Conference on Discovery Science (DS), pages 102-116. Springer, 2010.

[PDF]

J. R. Foulds and E. Frank. A review of multi-instance learning assumptions. Knowledge Engineering Review (KER), 25(1):1-25, 2010.

[PDF]

J. R. Foulds and E. Frank. Revisiting multiple-instance learning via embedded instance selection. Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence (AI), Auckland, New Zealand. Springer, 2008.

[PDF]

J. R. Foulds and L. R. Foulds, A probabilistic dynamic programming model of rape seed harvesting. International Journal of Operational Research (IJOR), Vol. 1, No. 4, 2006.

[Article]

J. R. Foulds and L. R. Foulds, Bridge lane direction specification for sustainable traffic management. Asia-Pacific Journal of Operational Research (APJOR), Vol. 23, No. 2, 2006.

[Article]

Theses

J. R. Foulds. Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques. Ph.D. Thesis, Department of Computer Science, University of California, Irvine, 2014.

[PDF]

[Bibtex]

J. R. Foulds. Learning Instance Weights in Multi-Instance Learning. MSc Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2008.

[PDF]

J. R. Foulds. Learning to play the game of go. Honours Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2006.

[PDF]