1 code implementation • 22 Apr 2024 • Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
Our main finding is that, in general, approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (i. e., employ a "negative gradient") outperform offline and maximum likelihood objectives.
1 code implementation • 12 Oct 2023 • Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn
Can we leverage offline RL to recover better policies from online interaction?
1 code implementation • 8 Jun 2023 • Caroline Choi, Fahim Tajwar, Yoonho Lee, Huaxiu Yao, Ananya Kumar, Chelsea Finn
Taking inspiration from this result, we present data-driven confidence minimization (DCM), which minimizes confidence on an uncertainty dataset containing examples that the model is likely to misclassify at test time.
1 code implementation • 20 Oct 2022 • Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn
A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task.
1 code implementation • 19 Oct 2022 • Annie Xie, Fahim Tajwar, Archit Sharma, Chelsea Finn
A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world.
1 code implementation • ICLR 2022 • Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn
Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks.
Ranked #21 on Long-tail Learning on CIFAR-10-LT (ρ=100)
1 code implementation • 12 Sep 2021 • Fahim Tajwar, Ananya Kumar, Sang Michael Xie, Percy Liang
Out-of-distribution detection is an important component of reliable ML systems.
Out-of-Distribution Detection Out of Distribution (OOD) Detection