| Untangling the seasonal dynamics of plant-pollinator communities B Bramon Mora, E Shin, PJ CaraDonna, DB Stouffer Nature Communications 11 (1), 4086, 2020 | 31 | 2020 |
| Learning from non-experts: an interactive and adaptive learning approach for appliance recognition in smart homes J Codispoti, AR Khamesi, N Penn, S Silvestri, E Shin ACM Transactions on Cyber-Physical Systems (TCPS) 6 (2), 1-22, 2022 | 12 | 2022 |
| Machine learning in the wild: The case of user-centered learning in cyber physical systems AR Khamesi, E Shin, S Silvestri 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS …, 2020 | 12 | 2020 |
| A user-centered active learning approach for appliance recognition E Shin, AR Khamesi, Z Bahr, S Silvestri, DA Baker 2020 IEEE International Conference on Smart Computing (SMARTCOMP), 208-213, 2020 | 9 | 2020 |
| Modeling mobile health users as reinforcement learning agents E Shin, S Swaroop, W Pan, S Murphy, F Doshi-Velez arXiv preprint arXiv:2212.00863, 2022 | 5 | 2022 |
| Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning LL Ankile, BS Ham, K Mao, E Shin, S Swaroop, F Doshi-Velez, W Pan arXiv preprint arXiv:2307.08169, 2023 | 3 | 2023 |
| Design and evaluation of a hair combing system using a general-purpose robotic arm N Dennler, E Shin, M Matarić, S Nikolaidis 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 3 | 2021 |
| Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks E Nofshin, S Swaroop, W Pan, S Murphy, F Doshi-Velez arXiv preprint arXiv:2401.14923, 2024 | 2 | 2024 |
| Online model selection by learning how compositional kernels evolve E Shin, P Klasnja, SA Murphy, F Doshi-Velez Transactions on machine learning research 2023, 2023 | 2 | 2023 |
| Efficient learning in computer-aided diagnosis through label propagation S Berglin, E Shin, J Furst, D Raicu Medical Imaging 2019: Computer-Aided Diagnosis 10950, 380-390, 2019 | 1 | 2019 |
| A Sim2Real Approach for Identifying Task-Relevant Properties in Interpretable Machine Learning E Nofshin, E Brown, B Lim, W Pan, F Doshi-Velez arXiv preprint arXiv:2406.00116, 2024 | | 2024 |
| Leveraging Interpretable Human Models to Personalize AI Interventions for Behavior Change E Nofshin Proceedings of the 23rd International Conference on Autonomous Agents and …, 2024 | | 2024 |
| Online structural kernel selection for mobile health E Shin, P Klasnja, S Murphy, F Doshi-Velez arXiv preprint arXiv:2107.09949, 2021 | | 2021 |
| Expanding annotated data with informed labels for weak supervision. E Shin, S Berglin, J Furst, D Raicu MLDM (2), 571-584, 2019 | | 2019 |
| AMBER: An Entropy Maximizing Environment Design Algorithm for Inverse Reinforcement Learning P Nitschke, LL Ankile, E Nofshin, S Swaroop, F Doshi-Velez, W Pan ICML 2024 Workshop on Models of Human Feedback for AI Alignment, 0 | | |
| Discovering User Types: Characterization of User Traits by Task-Specific Behaviors in Reinforcement Learning LL Ankile, B Ham, K Mao, E Shin, S Swaroop, F Doshi-Velez, W Pan | | |
