3 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
EXAGREE: Mitigating Explanation Disagreement with Stakeholder-Aligned Models
Sichao Li, Tommy Liu, Quanling Deng +1
Conflicting explanations, arising from different attribution methods or model internals, limit the adoption of machine learning models in safety-critical domains. We turn this disa…
Practical Attribution Guidance for Rashomon Sets
Sichao Li, Amanda S. Barnard, Quanling Deng
Different prediction models might perform equally well (Rashomon set) in the same task, but offer conflicting interpretations and conclusions about the data. The Rashomon effect in…
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences
Sichao Li, Xin Wang, Amanda Barnard
Machine learning methods have been remarkably successful in material science, providing novel scientific insights, guiding future laboratory experiments, and accelerating materials…
Exploring the cloud of feature interaction scores in a Rashomon set
Sichao Li, Rong Wang, Quanling Deng +1
Interactions among features are central to understanding the behavior of machine learning models. Recent research has made significant strides in detecting and quantifying feature…
Variance Tolerance Factors For Interpreting ALL Neural Networks
Sichao Li, Amanda Barnard
Black box models only provide results for deep learning tasks, and lack informative details about how these results were obtained. Knowing how input variables are related to output…