| Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and Trends® in Machine Learning 14 (1–2), 1-210, 2021 | 3093 | 2021 |
| Student Success Prediction in MOOCs J Gardner, C Brooks User Modeling and User-Adapted Interaction 28 (2), 127-203, 2018 | 167 | 2018 |
| Evaluating the fairness of predictive student models through slicing analysis J Gardner, C Brooks, R Baker Proceedings of the 9th international conference on learning analytics …, 2019 | 123 | 2019 |
| Mariana Raykova, Dawn Song, Weikang Song, Sebastian U P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth …, 2021 | 60 | 2021 |
| Mlperf inference benchmark. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... Valencia, Spain, 446-459, 2020 | 42 | 2020 |
| Replicating MOOC predictive models at scale J Gardner, C Brooks, JM Andres, R Baker Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 1-10, 2018 | 28 | 2018 |
| Dropout model evaluation in MOOCs J Gardner, C Brooks Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 25 | 2018 |
| MORF: A framework for predictive modeling and replication at scale with privacy-restricted mooc data J Gardner, C Brooks, JM Andres, RS Baker 2018 IEEE International Conference on Big Data (Big Data), 3235-3244, 2018 | 21 | 2018 |
| Mt3: Multi-task multitrack music transcription J Gardner, I Simon, E Manilow, C Hawthorne, J Engel arXiv preprint arXiv:2111.03017, 2021 | 19 | 2021 |
| Modeling and Experimental Design for MOOC Dropout Prediction: A Replication Perspective. J Gardner, Y Yang, RS Baker, C Brooks International Educational Data Mining Society, 2019 | 19 | 2019 |
| Evaluating predictive models of student success: Closing the methodological gap J Gardner, C Brooks Journal of Learning Analytics 5 (2), 105-125, 2018 | 19 | 2018 |
| Coenrollment networks and their relationship to grades in undergraduate education J Gardner, C Brooks Proceedings of the 8th international conference on learning analytics and …, 2018 | 16 | 2018 |
| How gender cues in educational video impact participation and retention C Brooks, J Gardner, K Chen International Society of the Learning Sciences, Inc.[ISLS]., 2018 | 14 | 2018 |
| Beyond A/B testing: Sequential randomization for developing interventions in scaled digital learning environments T NeCamp, J Gardner, C Brooks Proceedings of the 9th International Conference on learning analytics …, 2019 | 12 | 2019 |
| Enabling End-To-End machine learning replicability: A case study in educational data mining J Gardner, Y Yang, R Baker, C Brooks arXiv preprint arXiv:1806.05208, 2018 | 10 | 2018 |
| Driving with data: Modeling and forecasting vehicle fleet maintenance in Detroit J Gardner, D Koutra, J Mroueh, V Pang, A Farahi, S Krassenstein, J Webb arXiv preprint arXiv:1710.06839, 2017 | 10 | 2017 |
| Multi-instrument music synthesis with spectrogram diffusion C Hawthorne, I Simon, A Roberts, N Zeghidour, J Gardner, E Manilow, ... arXiv preprint arXiv:2206.05408, 2022 | 9 | 2022 |
| A statistical framework for predictive model evaluation in MOOCs J Gardner, C Brooks Proceedings of the Fourth (2017) ACM Conference on Learning@ Scale, 269-272, 2017 | 9 | 2017 |
| Learn from Your (Markov) Neighbour: Co-Enrollment, Assortativity, and Grade Prediction in Undergraduate Courses. J Gardner, C Brooks, W Li Journal of Learning Analytics 5 (3), 42-59, 2018 | 8 | 2018 |
| Statistical Approaches to the Model Comparison Task in Learning Analytics J Gardner, C Brooks Workshop on Methodology in Learning Analytics (MLA) at 7th International …, 2017 | 8 | 2017 |
Christopher A. BrooksUniversity of MichiganVerifisert e-postadresse på umich.edu
