| 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 | 5974 | 2021 |
| Openflamingo: An open-source framework for training large autoregressive vision-language models A Awadalla, I Gao, J Gardner, J Hessel, Y Hanafy, W Zhu, K Marathe, ... arXiv preprint arXiv:2308.01390, 2023 | 325 | 2023 |
| Student Success Prediction in MOOCs J Gardner, C Brooks User Modeling and User-Adapted Interaction 28 (2), 127-203, 2018 | 237 | 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 | 188 | 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 | 185 | 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, ... IEEE, 2020 | 83 | 2020 |
| MT3: Multi-task multitrack music transcription J Gardner, I Simon, E Manilow, C Hawthorne, J Engel arXiv preprint arXiv:2111.03017, 2021 | 82 | 2021 |
| 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 | 50 | 2022 |
| Visit-bench: A benchmark for vision-language instruction following inspired by real-world use Y Bitton, H Bansal, J Hessel, R Shao, W Zhu, A Awadalla, J Gardner, ... arXiv preprint arXiv:2308.06595, 2023 | 35 | 2023 |
| 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 | 35 | 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 | 28 | 2018 |
| Dropout model evaluation in MOOCs J Gardner, C Brooks Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 28 | 2018 |
| 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 | 23 | 2019 |
| Evaluating predictive models of student success: Closing the methodological gap J Gardner, C Brooks Journal of Learning Analytics 5 (2), 105-125, 2018 | 23 | 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 | 22 | 2018 |
| Llark: A multimodal foundation model for music J Gardner, S Durand, D Stoller, RM Bittner arXiv preprint arXiv:2310.07160, 2023 | 18 | 2023 |
| 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 | 18 | 2018 |
| Subgroup robustness grows on trees: An empirical baseline investigation J Gardner, Z Popovic, L Schmidt Advances in Neural Information Processing Systems 35, 9939-9954, 2022 | 16 | 2022 |
| 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 | 16 | 2018 |
| Benchmarking distribution shift in tabular data with tableshift J Gardner, Z Popovic, L Schmidt Advances in Neural Information Processing Systems 36, 2024 | 14 | 2024 |
Christopher A. BrooksUniversity of MichiganEmail verificata su umich.edu
