Peer-Reviewed Conference and Journal Papers
* denotes equal contribution or advising
Why Do Decision Makers (Not) Use AI? A Cross-Domain Analysis of Factors Impacting AI Adoption
Rebecca Yu, Valerie Chen, Ameet Talwalkar, Hoda Heidari
AIES, 2025
When Benchmarks Talk: Re-Evaluating Code LLMs with Interactive Feedback
Jane Pan*, Ryan Shar*, Jacob Pfau, Ameet Talwalkar, He He**, and Valerie Chen**
ACL Findings, 2025
Copilot Arena: A Platform for Code LLM Evaluation in the Wild
Wayne Chi*, Valerie Chen*, Anastasios Nikolas Angelopoulos, Wei-Lin Chiang, Aditya Mittal, Naman Jain, Tianjun Zhang, Ion Stoica, Chris Donahue, Ameet Talwalkar
ICML, 2025
Need Help? Designing Proactive AI Assistants for Programming
Valerie Chen, Alan Zhu, Sebastian Zhao, Hussein Mozannar, David Sontag, Ameet Talwalkar
CHI, 2025
Learning Personalized Decision Support Policies
Umang Bhatt*, Valerie Chen*, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
AAAI, 2025
The RealHumanEval: Evaluating Large Language Models’ Abilities to Support Programmers
Hussein Mozannar*, Valerie Chen*, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David Sontag
TMLR, 2025 (Expert Certification)
Do LLMs Exhibit Human-Like Response Biases? A Case Study in Survey Design
Lindia Tjuatja*, Valerie Chen*, Tongshuang Wu, Ameet Talwalkar, Graham Neubig
TACL, 2024
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Giang Nguyen, Valerie Chen, Mohammad Reza Taesiri, Anh Totti Nguyen
TMLR, 2024 (J2C: full presentation at ICLR 2025)
Applying Interpretable Machine Learning in Computational Biology—Pitfalls, Recommendations and Opportunities for New Developments
Valerie Chen, Muyu Yang, Wenbo Cui, Joon Sik Kim, Ameet Talwalkar, Jian Ma
Nature Methods, 2024
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
AAAI, 2024
Understanding the Role of Human Intuition on Reliance in Human–AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
CSCW, 2023
Assisting Human Decisions in Document Matching
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
TMLR, 2024
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen*, Umang* Bhatt
EAAMO, 2023
Perspectives on Incorporating Expert Feedback into Model Updates
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
Patterns, 2023
Use-Case-Grounded Simulations for Explanation Evaluation
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
NeurIPS, 2022
Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Kim, Ameet Talwalkar, Hoda Heidari, Steven Z. Wu
NeurIPS, 2022
Interpretable Machine Learning: Moving from Mythos to Diagnostics
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Communications of the ACM, 2022
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
Valerie Chen, Abhinav Gupta, Kenneth Marino
ICLR, 2021
Task-Aware Novelty Detection for Visual-Based Deep Learning in Autonomous Systems
Valerie Chen, Man-Ki Yoon, Zhong Shao
ICRA, 2020