# Publications (see also my Google Scholar page)

#### 2020

**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]**

**An Intersectional Definition of Fairness.**36th IEEE International Conference on Data Engineering (

**ICDE**). ArXiv preprint arXiv:1807.08362 [CS.LG], 2020.

**[Arxiv preprint]**

#### 2019

- M. Park, J. R. Foulds, K. Chaudhuri, and M. Welling.
**Variational Bayes in private settings (VIPS)**. Journal of Artificial Intelligence Research (**JAIR**) (accepted, in press), ArXiv preprint arXiv:1611.00340 [stat.ML], 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]**[Code: DF metric implemented in the IBM Research AI Fairness 360 toolkit] [AIF 360 Github page]**[Arxiv long version]** - K. Keya, Y. Papanikolaou, and J. R. Foulds.
**Neural embedding allocation: Distributed representations of topic models.**ArXiv preprint arXiv:1909.04702 [cs.CL], 2019. - 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. - 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. - R. Islam and J. R. Foulds.
**Scalable collapsed inference for high-dimensional topic models.**In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (**NAACL**), 2019.

**[Arxiv preprint]**

**[Arxiv preprint]**

**[PDF]**

**[PDF]**

**[ACM digital library]**

**[Arxiv preprint]**

**[Bibtex]**

**[PDF]**

**[Bibtex]**

#### 2018

- T. Ghiwaa and J. R. Foulds.
**Training WGANs with peer instruction**. In Mid-Atlantic Student Colloquium on Speech, Language and Learning (**MASC-SLL**), 2018. - R. Islam and J. R. Foulds.
**Towards a highly efficient online inference algorithm for latent Dirichlet allocation.**In Mid-Atlantic Student Colloquium on Speech, Language and Learning (**MASC-SLL**), 2018. - K. Keya, Y. Papanikolaou, and J. R. Foulds.
**Neural embedding allocation: Distributed representations of words, topics, and documents.**In Mid-Atlantic Student Colloquium on Speech, Language and Learning (**MASC-SLL**), 2018. - 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]**

**[PDF]**

**[PDF]**

**[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. - 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]**

**[Arxiv preprint]**

**[Bibtex]**

**[Code]**

**[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. - J. R. Foulds.
**Mixed Membership Word Embeddings: Corpus-Specific Embeddings Without Big Data**. SoCal Machine Learning Symposium (**SCMLS**), 2016. - M. Park, J. R. Foulds, K. Chaudhuri, M. Welling.
**Private Topic Modeling**. NIPS Workshop on Private Multi-Party Machine Learning (**PMPML**), 2016.

**[PDF]**

**[Slides]**

**[Poster]**

**[ArXiv preprint]**

**[Bibtex]**

**Winner of the runner-up prize for best presentation!**

**[PDF]**

**[Slides]**

**[Poster]**

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

**[Bibtex]**

**[Data]**

**[PDF]**

**[Slides]**

**[Talk]**

**[PDF]**

**[Slides]**

**[Poster]**

**[PDF]**

**[Slides]**

**[Supplementary]**

[Code is available with the PSL system, and is currently in the develop branch. Please feel free to email me if you would like help using this code.]

**[PDF]**

**[Supplementary]**

**[PDF]**

**[Slides]**

**[Bibtex]**

**[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. - 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]**

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

**[PDF]**[Poster]

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

**[PDF]**

**[PDF]**

**[Code]**

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

**[PDF]**

**[PDF]**

**[PDF]**

**[Article]**

**[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. - J. R. Foulds.
**Learning Instance Weights in Multi-Instance Learning**. MSc Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2008. - J. R. Foulds.
**Learning to play the game of go**. Honours Thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand, 2006.

**[PDF]**

**[Bibtex]**

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