Fahim Tajwar
Fahim Tajwar
PhD Student, Machine Learning, Carnegie Mellon University
Verified email at - Homepage
Cited by
Cited by
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Y Lee, AS Chen, F Tajwar, A Kumar, H Yao, P Liang, C Finn
International Conference on Learning Representations (ICLR), 2023, 2022
Scalable deep learning to identify brick kilns and aid regulatory capacity
J Lee, NR Brooks, F Tajwar, M Burke, S Ermon, DB Lobell, D Biswas, ...
Proceedings of the National Academy of Sciences 118 (17), e2018863118, 2021
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
F Tajwar, A Kumar, SM Xie, P Liang
ICML Workshop on Uncertainty & Robustness in Deep Learning, 2021, 2021
Do Deep Networks Transfer Invariances Across Classes?
A Zhou, F Tajwar, A Robey, T Knowles, GJ Pappas, H Hassani, C Finn
International Conference on Learning Representations (ICLR), 2022, 2022
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
F Tajwar, A Singh, A Sharma, R Rafailov, J Schneider, T Xie, S Ermon, ...
International Conference on Machine Learning (ICML), 2024, 2024
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
A Xie, F Tajwar, A Sharma, C Finn
Neural Information Processing Systems (NeurIPS), 2022, 2022
Conservative Prediction via Data-Driven Confidence Minimization
C Choi, F Tajwar, Y Lee, H Yao, A Kumar, C Finn
arXiv preprint arXiv:2306.04974, 2023
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
MS Mark, A Sharma, F Tajwar, R Rafailov, S Levine, C Finn
arXiv preprint arXiv:2310.08558, 2023
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