9 citations · 11 across the 4 of their papers we have counts for
4 papers
Selecting Decision-Relevant Concepts in Reinforcement Learning
Naveen Raman, Stephanie Milani, Fei Fang
Training interpretable concept-based policies requires practitioners to manually select which human-understandable concepts an agent should reason with when making sequential decis…
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
Mian Zhang, Xianjun Yang, Xinlu Zhang +6
There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models…
LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning
Zhuorui Ye, Stephanie Milani, Geoffrey J. Gordon +1
Recent advances in reinforcement learning (RL) have predominantly leveraged neural network policies for decision-making, yet these models often lack interpretability, posing challe…
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
Ruiyi Wang, Stephanie Milani, Jamie C. Chiu +9
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and act…