| Deep multi-output forecasting: Learning to accurately predict blood glucose trajectories I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018 | 103 | 2018 |
| The ELFIN mission V Angelopoulos, E Tsai, L Bingley, C Shaffer, DL Turner, A Runov, W Li, ... Space science reviews 216, 1-45, 2020 | 98 | 2020 |
| Deep reinforcement learning for closed-loop blood glucose control I Fox, J Lee, R Pop-Busui, J Wiens Machine Learning for Healthcare Conference, 508-536, 2020 | 69 | 2020 |
| Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction. H Rubin-Falcone, I Fox, J Wiens KDH@ ECAI 20, 105-109, 2020 | 56 | 2020 |
| Reinforcement learning for blood glucose control: Challenges and opportunities I Fox, J Wiens | 38 | 2019 |
| The advantage of doubling: a deep reinforcement learning approach to studying the double team in the NBA J Wang, I Fox, J Skaza, N Linck, S Singh, J Wiens arXiv preprint arXiv:1803.02940, 2018 | 28 | 2018 |
| Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management; University of Michigan IG Fox ProQuest Dissertations Publishing, 2020 | 23 | 2020 |
| Contextual motifs: Increasing the utility of motifs using contextual data I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 20 | 2017 |
| Cell list algorithms for nonequilibrium molecular dynamics M Dobson, I Fox, A Saracino Journal of Computational Physics 315, 211-220, 2016 | 13 | 2016 |
| Deep Multi-Output Forecasting I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 7 | 2018 |
| Association between management of continuous subcutaneous basal insulin administration and HbA1C H Rubin-Falcone, I Fox, E Hirschfeld, L Ang, R Pop-Busui, JM Lee, ... Journal of Diabetes Science and Technology 16 (5), 1120-1127, 2022 | 4 | 2022 |
| Advocacy learning: Learning through competition and class-conditional representations I Fox, J Wiens arXiv preprint arXiv:1908.02723, 2019 | 3 | 2019 |
| Machine learning for physiological time series: Representing and controlling blood glucose for diabetes management I Fox | 2 | 2020 |
| Learning through limited self-supervision: Improving time-series classification without additional data via auxiliary tasks I Fox, H Rubin-Falcone, J Wiens | 2 | 2019 |
| Personalized execution time optimization for the scheduled jobs Y Liu, J Wang, Z Chen, I Fox, I Mufti, J Sukumaran, B He, X Sun, F Liang arXiv preprint arXiv:2203.06158, 2022 | 1 | 2022 |
| Contextual Motifs I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 1 | 2017 |
| Atmospheric scattering of energetic electrons from near-Earth space V Angelopoulos, E Tsai, C Wilkins, X Zhang, A Artemyev, J Liu, A Runov, ... | | 2021 |
| Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities I Fox, J Lee, R Busui, J Wiens | | |
| Advocacy Learning I Fox, J Wiens | | |