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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
About me
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
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publications
Fairness-Aware Mixture of Experts with Interpretability Budgets
Published in International Conference on Discovery Science (DS 2023), 2023
In this paper, we argue that interpretability should not be viewed as a binary aspect and that, instead, it should be viewed as a continuous domain-informed notion. With this aim, we leverage the well-known Mixture of Experts architecture with user-defined budgets for the controlled use of non-interpretable models. We extend this idea with a counterfactual fairness module to ensure the selection of consistently fair experts: FairMOE.
Recommended citation: Germino, J., Moniz, N., Chawla, N.V. (2023). Fairness-Aware Mixture of Experts with Interpretability Budgets. In: Bifet, A., Lorena, A.C., Ribeiro, R.P., Gama, J., Abreu, P.H. (eds) Discovery Science. DS 2023. Lecture Notes in Computer Science(), vol 14276. Springer, Cham. https://doi.org/10.1007/978-3-031-45275-8_23 https://doi.org/10.1007/978-3-031-45275-8_23
A Community Focused Approach Towards Making Healthy and Affordable Daily Diet Recommendations
Published in Frontiers in Big Data, 6, 2023
The goal of this paper is to demonstrate how the integration of data from local grocery stores and federal government databases can be used to assist specific communities in meeting their unique health and budget challenges.
Recommended citation: Germino, J., Szymanski, A., Metoyer, R., & Chawla, N. V. A Community Focused Approach Towards Making Healthy and Affordable Daily Diet Recommendations. Frontiers in Big Data, 6, 1086212. https://doi.org/10.3389/fdata.2023.1086212
FairMOE: counterfactually-fair mixture of experts with levels of interpretability
Published in Machine Learning, 2024
In this paper, we extend our previous work arguing that interpretability should not be viewed as a binary aspect and that, instead, it should be viewed as a continuous domain-informed notion. Building on our prior work, we leverage the well-known Mixture of Experts architecture with a counterfactual fairness module to ensure the selection of consistently fair experts: FairMOE. We expand on the previous paper with a detailed analysis on the assignment of predictions to gain more insights into the strengths and weaknesses of the individual experts.
Recommended citation: Germino, Joe, Nuno Moniz, and Nitesh V. Chawla. "FairMOE: counterfactually-fair mixture of experts with levels of interpretability." Machine Learning (2024): 1-21. https://doi.org/10.1007/978-3-031-45275-8_23
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Data Science
Undergraduate/Graduate course, University of Notre Dame, Computer Science and Engineering, 2023
Instructor of record for the Summer 2023 Data Science course taught at the University of Notre Dame in the Department of Computer Science and Engineering. The course was offered to both undergraduate and graduate students.
Elements of Computing I
Undergraduate course, University of Notre Dame, Computer Science and Engineering, 2024
Instructor of record for the Spring 2024 Elements of Computing I course taught at the University of Notre Dame in the Department of Computer Science and Engineering. The class is an introduction to core programming skills for non-computer science majors culminating in a final project where students designed a game in the python language. Responsibilities included syllabus design, preparing class lectures, and overall classroom management.