activity
20182025
most citedWay Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog

134 citations · 164 across the 14 of their papers we have counts for

collaborators

21 papers

cs.CY2025

Societal Impacts Research Requires Benchmarks for Creative Composition Tasks

Judy Hanwen Shen, Carlos Guestrin

Foundation models that are capable of automating cognitive tasks represent a pivotal technological shift, yet their societal implications remain unclear. These systems promise exci…

cs.LG2025

Two Tickets are Better than One: Fair and Accurate Hiring Under Strategic LLM Manipulations

Lee Cohen, Jack Hsieh, Connie Hong +1

In an era of increasingly capable foundation models, job seekers are turning to generative AI tools to enhance their application materials. However, unequal access to and knowledge…

stat.ML2025

Algorithms with Calibrated Machine Learning Predictions

Judy Hanwen Shen, Ellen Vitercik, Anders Wikum

The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the e…

cs.AI2024

Towards Data-Centric RLHF: Simple Metrics for Preference Dataset Comparison

Judy Hanwen Shen, Archit Sharma, Jun Qin

The goal of aligning language models to human preferences requires data that reveal these preferences. Ideally, time and money can be spent carefully collecting and tailoring bespo…

cs.LG2024★ 4 cited

The Data Addition Dilemma

Judy Hanwen Shen, Inioluwa Deborah Raji, Irene Y. Chen

In many machine learning for healthcare tasks, standard datasets are constructed by amassing data across many, often fundamentally dissimilar, sources. But when does adding more da…

cs.LG2024

Multigroup Robustness

Lunjia Hu, Charlotte Peale, Judy Hanwen Shen

To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical proce…