Valerie Chen

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 31

Top Topics

Machine Learning

Experimental Designs

Top Venues

Publications

Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. AAAI (2024)
Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. CoRR (2024)
Katherine M. Collins, Valerie Chen, Ilia Sucholutsky, Hannah Rose Kirk, Malak Sadek, Holli Sargeant, Ameet Talwalkar, Adrian Weller, Umang Bhatt
Modulating Language Model Experiences through Frictions. CoRR (2024)
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
Perspectives on incorporating expert feedback into model updates. Patterns 4 (7) (2023)
Ada Martin, Valerie Chen, Sérgio M. Jesus, Pedro Saleiro
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies. CoRR (2023)
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. EAAMO (2023)
Matthew Barker, Emma Kallina, Dhananjay Ashok, Katherine M. Collins, Ashley Casovan, Adrian Weller, Ameet Talwalkar, Valerie Chen, Umang Bhatt
FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelines. CoRR (2023)
Lindia Tjuatja, Valerie Chen, Sherry Tongshuang Wu, Ameet Talwalkar, Graham Neubig
Do LLMs exhibit human-like response biases? A case study in survey design. CoRR (2023)
Valerie Chen
Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions. AIES (2023)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. CoRR (2023)
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
Assisting Human Decisions in Document Matching. Trans. Mach. Learn. Res. 2023 (2023)
Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
Assisting Human Decisions in Document Matching. CoRR (2023)
Umang Bhatt, Valerie Chen, Katherine M. Collins, Parameswaran Kamalaruban, Emma Kallina, Adrian Weller, Ameet Talwalkar
Learning Personalized Decision Support Policies. CoRR (2023)
Giang Nguyen, Valerie Chen, Anh Nguyen
AdvisingNets: Learning to Distinguish Correct and Wrong Classifications via Nearest-Neighbor Explanations. CoRR (2023)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. Proc. ACM Hum. Comput. Interact. 7 (CSCW2) (2023)
Kasun Amarasinghe, Kit T. Rodolfa, Sérgio M. Jesus, Valerie Chen, Vladimir Balayan, Pedro Saleiro, Pedro Bizarro, Ameet Talwalkar, Rayid Ghani
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods. CoRR (2022)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. CoRR (2022)
Valerie Chen, Umang Bhatt, Hoda Heidari, Adrian Weller, Ameet Talwalkar
Perspectives on Incorporating Expert Feedback into Model Updates. CoRR (2022)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Interpretable machine learning: moving from mythos to diagnostics. Commun. ACM 65 (8) (2022)
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
Bayesian Persuasion for Algorithmic Recourse. NeurIPS (2022)
Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar
Use-Case-Grounded Simulations for Explanation Evaluation. NeurIPS (2022)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Interpretable Machine Learning: Moving from mythos to diagnostics. ACM Queue 19 (6) (2021)
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Towards Connecting Use Cases and Methods in Interpretable Machine Learning. CoRR (2021)
Valerie Chen, Abhinav Gupta, Kenneth Marino
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning. ICLR (2021)
Keegan Harris, Valerie Chen, Joon Sik Kim, Ameet Talwalkar, Hoda Heidari, Zhiwei Steven Wu
Bayesian Persuasion for Algorithmic Recourse. CoRR (2021)
Valerie Chen, Man-Ki Yoon, Zhong Shao
Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems. ICRA (2020)
Valerie Chen, Abhinav Gupta, Kenneth Marino
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning. CoRR (2020)
Valerie Chen, Man-Ki Yoon, Zhong Shao
Novelty Detection via Network Saliency in Visual-Based Deep Learning. DSN Workshops (2019)
Valerie Chen, Man-Ki Yoon, Zhong Shao
Novelty Detection via Network Saliency in Visual-based Deep Learning. CoRR (2019)
Valerie Chen, Valerio Pastro, Mariana Raykova
Secure Computation for Machine Learning With SPDZ. CoRR (2019)
Mayoore Jaiswal, H. Peter Hofstee, Valerie Chen, Suvadip Paul, Rogério Feris, Frank Liu, Anupama Jagannathan, Anne Gattiker, Inseok Hwang, Jinho Lee, Matthew Tong, Sahil Dureja, Soham Shah
Video-Text Compliance: Activity Verification Based on Natural Language Instructions. ICCV Workshops (2019)