Fahim Tajwar

I am a PhD Student at the Machine Learning Department of Carnegie Mellon University, co-advised by Prof. Ruslan Salakhutdinov and Prof. Jeff Schneider. I also collaborate regularly with Prof. Aviral Kumar.

Previously, I obtained my BS (with distinction, Mathemetics) in 2022 and MS (Computer Science) in 2023 from Stanford University. There I am grateful to have my research supervised by Prof. Chelsea Finn. I am also fortunate to have worked with Prof. Percy Liang, Prof. Stefano Ermon, and Prof. Stephen Luby during my time at Stanford.

Feel free to reach out to me in case you have any questions or want to chat about my work!

Email  /  CV  /  Google Scholar  /  Github  /  LinkedIn

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Conference Publications
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar*, Anikait Singh*, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, and Aviral Kumar
International Conference on Machine Learning (ICML), 2024
[Paper], [Code], [Project Website]
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Yoonho Lee*, Annie S Chen*, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn
International Conference on Learning Representations (ICLR), 2023
[Paper], [Code]
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
Annie Xie*, Fahim Tajwar*, Archit Sharma*, and Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
[Paper], [Code], [Project Website]
Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou*, Fahim Tajwar*, Alexander Robey, Tom Knowles, George J Pappas, Hamed Hassani, and Chelsea Finn
International Conference on Learning Representations (ICLR), 2022
[Paper], [Code]
Journal Publications
Conservative Prediction via Data-Driven Confidence Minimization
Caroline Choi*, Fahim Tajwar*, Yoonho Lee*, Huaxiu Yao, Ananya Kumar, and Chelsea Finn
Transactions on Machine Learning Research (TMLR), 2024
[Paper], [Code]
Scalable deep learning to identify brick kilns and aid regulatory capacity
Jihyeon Lee*, Nina R. Brooks*, Fahim Tajwar, Marshall Burke, Stefano Ermon, David B. Lobell, Debashish Biswas, and Stephen Luby
Proceedings of the National Academy of Sciences (PNAS), 2021
[Paper], [Code]
Preprints/Workshop Publications
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Max Sobol Mark*, Archit Sharma*, Fahim Tajwar, Rafael Rafailov, Sergey Levine, and Chelsea Finn
Preprint, 2023
[Paper], [Code]
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Fahim Tajwar, Ananya Kumar*, Sang Michael Xie*, and Percy Liang
ICML Workshop on Uncertainty & Robustness in Deep Learning (UDL), 2021
[Paper], [Code]

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