activity
20192025
most citedWay Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

134 citations · 217 across the 5 of their papers we have counts for

collaborators

6 papers

cs.HC2025★ 3 cited

Connecting through Comics: Design and Evaluation of Cube, an Arts-Based Digital Platform for Trauma-Impacted Youth

Ila Kumar, Jocelyn Shen, Craig Ferguson +1

This paper explores the design, development and evaluation of a digital platform that aims to assist young people who have experienced trauma in understanding and expressing their…

cs.HC2025★ 4 cited

Cultivating a Supportive Sphere: Designing Technology to Increase Social Support for Foster-Involved Youth

Ila Kumar, Craig Ferguson, Jiayi Wu +1

Approximately 400,000 youth in the US are living in foster care due to experiences with abuse or neglect at home. For multiple reasons, these youth often don't receive adequate soc…

cs.HC2022

Computational Empathy Counteracts the Negative Effects of Anger on Creative Problem Solving

Matthew Groh, Craig Ferguson, Robert Lewis +1

How does empathy influence creative problem solving? We introduce a computational empathy intervention based on context-specific affective mimicry and perspective taking by a virtu…

cs.CL2020

Human-centric Dialog Training via Offline Reinforcement Learning

Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun +5

How can we train a dialog model to produce better conversations by learning from human feedback, without the risk of humans teaching it harmful chat behaviors? We start by hosting…

cs.LG2019★ 134 cited

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen +5

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. These are critica…

cs.CL2019★ 76 cited

Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems

Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques +4

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation qua…