2.3k citations · 2.4k across the 19 of their papers we have counts for
11 papers · 1 filter
Reinforcement Learning via Implicit Imitation Guidance
Perry Dong, Alec M. Lessing, Annie S. Chen +1
We study the problem of sample efficient reinforcement learning, where prior data such as demonstrations are provided for initialization in lieu of a dense reward signal. A natural…
Calibrating Language Models with Adaptive Temperature Scaling
Johnathan Xie, Annie S. Chen, Yoonho Lee +2
The effectiveness of large language models (LLMs) is not only measured by their ability to generate accurate outputs but also by their calibration-how well their confidence scores…
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Johnathan Xie, Yoonho Lee, Annie S. Chen +1
Self-supervised learning excels in learning representations from large amounts of unlabeled data, demonstrating success across multiple data modalities. Yet, extending self-supervi…
RLVF: Learning from Verbal Feedback without Overgeneralization
Moritz Stephan, Alexander Khazatsky, Eric Mitchell +4
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements…
AutoFT: Learning an Objective for Robust Fine-Tuning
Caroline Choi, Yoonho Lee, Annie Chen +3
Foundation models encode rich representations that can be adapted to downstream tasks by fine-tuning. However, fine-tuning a model on one data distribution often degrades performan…
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts
Annie S. Chen, Yoonho Lee, Amrith Setlur +2
Effective machine learning models learn both robust features that directly determine the outcome of interest (e.g., an object with wheels is more likely to be a car), and shortcut…
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features
Annie S. Chen, Yoonho Lee, Amrith Setlur +2
Transfer learning with a small amount of target data is an effective and common approach to adapting a pre-trained model to distribution shifts. In some situations, target data lab…
You Only Live Once: Single-Life Reinforcement Learning
Annie S. Chen, Archit Sharma, Sergey Levine +1
Reinforcement learning algorithms are typically designed to learn a performant policy that can repeatedly and autonomously complete a task, usually starting from scratch. However,…
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Yoonho Lee, Annie S. Chen, Fahim Tajwar +4
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 n…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Just Train Twice: Improving Group Robustness without Training Group Information
Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen +5
Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of s…