Joshua P. Gardner

Preprints

MT3: Multi-Task Multitrack Music Transcription
Josh Gardner, Ian Simon, Ethan Manilow, Curtis Hawthorne, Jesse Engel.
[arXiv] [web] [blog]

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

Journal and Conference Papers

	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]
	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]
	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]
	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]
	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]
	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]
	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]
	Evaluating Predictive Models of Student Success: Closing the Methodological Gap. Josh Gardner, Christopher Brooks. The Journal of Learning Analytics. [PDF]
	Student Success Prediction in MOOCs Josh Gardner, Christopher Brooks User Modeling and User-Adapted Interaction (UMUAI): The Journal of Personalization Research [PDF]
	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]
	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]
	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]
	Dropout Model Evaluation in MOOCs Josh Gardner, Christopher Brooks Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18) [PDF]

Workshop Papers (Peer-Reviewed)

	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]
	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;