49 citations · 49 across the 5 of their papers we have counts for
4 papers · 1 filter
Improving Reproducibility in Evaluation through Multi-Level Annotator Modeling
Deepak Pandita, Flip Korn, Chris Welty +1
As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of these systems is paramount. Howe…
Forest vs Tree: The Trade-off in Reproducible ML Evaluation
Deepak Pandita, Flip Korn, Chris Welty +1
Reproducibility is a cornerstone of scientific validation and of the authority it confers on its results. Reproducibility in machine learning evaluations leads to greater trust, co…
How Many Ratings per Item are Necessary for Reliable Significance Testing?
Christopher Homan, Flip Korn, Deepak Pandita +1
A cornerstone of machine learning evaluation is the (often hidden) assumption that model and human responses are reliable enough to evaluate models against unitary, authoritative,…
Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values
Yurong Liu, R. Teal Witter, Flip Korn +4
Banzhaf values provide a popular, interpretable alternative to the widely-used Shapley values for quantifying the importance of features in machine learning models. Like Shapley va…