2 citations · 3 across the 3 of their papers we have counts for
3 papers
What Is Wrong with My Model? Identifying Systematic Problems with Semantic Data Slicing
Chenyang Yang, Yining Hong, Grace A. Lewis +2
Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes. Practitioners engage in various activities, including…
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning +3
Current model testing work has mostly focused on creating test cases. Identifying what to test is a step that is largely ignored and poorly supported. We propose Weaver, an interac…
The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products
Nadia Nahar, Haoran Zhang, Grace Lewis +2
Machine learning (ML) components are increasingly incorporated into software products for end-users, but developers face challenges in transitioning from ML prototypes to products.…