Mitigating covariate shift in imitation learning via offline data with partial coverage J Chang, M Uehara, D Sreenivas, R Kidambi, W Sun Advances in Neural Information Processing Systems 34, 965-979, 2021 | 47 | 2021 |
Mobile: Model-based imitation learning from observation alone R Kidambi, J Chang, W Sun Advances in Neural Information Processing Systems 34, 28598-28611, 2021 | 31 | 2021 |
Mitigating covariate shift in imitation learning via offline data without great coverage JD Chang, M Uehara, D Sreenivas, R Kidambi, W Sun arXiv preprint arXiv:2106.03207, 2021 | 22 | 2021 |
Learning deep parameterized skills from demonstration for re-targetable visuomotor control J Chang, N Kumar, S Hastings, A Gokaslan, D Romeres, D Jha, ... arXiv preprint arXiv:1910.10628, 2019 | 13 | 2019 |
Learning to generate better than your llm JD Chang, K Brantley, R Ramamurthy, D Misra, W Sun arXiv preprint arXiv:2306.11816, 2023 | 8 | 2023 |
Learning bellman complete representations for offline policy evaluation J Chang, K Wang, N Kallus, W Sun International Conference on Machine Learning, 2938-2971, 2022 | 7 | 2022 |
Using unsupervised clustering to identify pregnancy co-morbidities J Chang, IN Sarkar AMIA Summits on Translational Science Proceedings 2019, 305, 2019 | 6 | 2019 |
Using self organizing maps to compare sepsis patients from the neonatal and adult intensive care unit B Goddard, J Chang, IN Sarkar AMIA Summits on Translational Science Proceedings 2019, 127, 2019 | 2 | 2019 |
Adversarial Imitation Learning via Boosting J Chang, D Sreenivas, Y Huang, K Brantley, W Sun International Conference on Learning Representations, 2024 | | 2024 |
Policy-Gradient Training of Language Models for Ranking G Gao, JD Chang, C Cardie, K Brantley, T Joachim arXiv preprint arXiv:2310.04407, 2023 | | 2023 |