Preprints

Characterizing the Representation Disparity of Differential Privacy.
Josh Gardner, Jamie Morgenstern, and Sewoong Oh.
In submission; arXiv soon.


Journal and Conference Papers

FL 2019

Advances and Open Problems in Federated Learning.
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu and Sen Zhao.
Foundations and Trends® in Machine Learning (2021): Vol. 14: No. 1-2, pp 1-210.
[doi]

Detroit 2020

Driving with Data in the Motor City: Mining and Modeling Vehicle Fleet Maintenance Data.
Josh Gardner, Jawad Mroueh, Natalia Jenuwine, Noah Weaverdyck, Samuel Krassenstein, Arya Farahi, Danai Koutra.
IEEE International Conference on Data Science and Advanced Analytics (DSAA20).
[arXiv]

EDM 19 Model Comparison

Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective.
Josh Gardner, Yuming Yang, Ryan Baker and Christopher Brooks.
Proceedings of the 12th International Conference on Educational Data Mining (EDM19).
[PDF]

LAK 19 Slicing

Evaluating the Fairness of Predictive Student Models Through Slicing Analysis
Josh Gardner, Christopher Brooks, and Ryan Baker.
Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK19).
Best Paper Award
[PDF]

LAK 19 SeqRand

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments.
Timothy NeCamp, Josh Gardner, Christopher Brooks.
Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK19).
[PDF]

IEEE BD19

MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data.
Josh Gardner, Miguel Andres-Bray, Christopher Brooks, and Ryan Baker.
Proceedings of the 2018 IEEE International Conference on Big Data. Presented at 3rd Workshop on Open Science in Big Data (OSBD).
[PDF]

JLA coenrollment 18

Learn From Your (Markov) Neighbor: Coenrollment, Assortativity, and Grade Prediction in Undergraduate Courses.
Josh Gardner, Christopher Brooks, and Warren Li.
The Journal of Learning Analytics.
[PDF]

JLA evaluation 19

Evaluating Predictive Models of Student Success: Closing the Methodological Gap.
Josh Gardner, Christopher Brooks.
The Journal of Learning Analytics.
[PDF]

UMUAI 18

Student Success Prediction in MOOCs
Josh Gardner, Christopher Brooks
User Modeling and User-Adapted Interaction (UMUAI): The Journal of Personalization Research
[PDF]

ICLS 18

How Gender Cues in Educational Video Impact Participation and Retention
Christopher Brooks, Josh Gardner, Kaifeng Chen.
Proceedings of the 2018 International Conference on the Learning Sciences (ICLS).
[PDF]

L@S 18

Replicating MOOC Predictive Models at Scale.
Josh Gardner, Christopher Brooks, Juan Miguel Andres, Ryan S. Baker
Proceedings of the Fifth ACM Conference on Learning@Scale (L@S).
[PDF]

LAK 18

Coenrollment Networks and their Relationship to Grades in Undergraduate Education
Josh Gardner, Christopher Brooks
Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK18)
[PDF]

EAAI 18

Dropout Model Evaluation in MOOCs
Josh Gardner, Christopher Brooks
Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18)
[PDF]

D4GX 17

Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit
Josh Gardner, Danai Koutra, Jawad Mroueh, Victor Pang, Arya Farahi, Sam Krassenstein, and Jared Webb
Bloomberg Data For Good Exchange (D4GX). Also appeared in Data Science for Social Good Conference (DSSG).
[PDF]


Workshop Papers and Works in Progress (Peer-Reviewed)

CV holdout comparison

Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining
Josh Gardner, Yuming Yang, Ryan S. Baker, and Christopher Brooks
Workshop on Enabling Reproducibility in Machine Learning at the Thirty-fifth International Conference on Machine Learning (REPML@ICML).
[PDF]

LAS 17

A Statistical Framework for Predictive Model Evaluation in MOOCs
Josh Gardner, Christopher Brooks
Fourth Annual Meeting of the ACM Conference on Learning@Scale (L@S)
[PDF]

Predictive Models with the Coenrollment Graph: Network-Based Grade Prediction in Undergraduate Courses
Josh Gardner, Christopher Brooks
The Third International Workshop on Graph-Based Educational Data Mining at 10th International Conference on Educational Data Mining

Statistical Approaches to the Model Comparison Task in Learning Analytics
Josh Gardner, Christopher Brooks
Workshop on Methodology in Learning Analytics (MLA) at 7th International Conference on Learning Analytics and Knowledge;