2 citations · 2 across the 4 of their papers we have counts for
4 papers
Direct Preference Optimization for Adaptive Concept-based Explanations
Jacopo Teneggi, Zhenzhen Wang, Paul H. Yi +2
Concept-based explanation methods aim at making machine learning models more transparent by finding the most important semantic features of an input (e.g., colors, patterns, shapes…
Multiaccuracy and Multicalibration via Proxy Groups
Beepul Bharti, Mary Versa Clemens-Sewall, Paul H. Yi +1
As the use of predictive machine learning algorithms increases in high-stakes decision-making, it is imperative that these algorithms are fair across sensitive groups. However, mea…
Sufficient and Necessary Explanations (and What Lies in Between)
Beepul Bharti, Paul Yi, Jeremias Sulam
As complex machine learning models continue to find applications in high-stakes decision-making scenarios, it is crucial that we can explain and understand their predictions. Post-…
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors
Beepul Bharti, Paul Yi, Jeremias Sulam
As the use of machine learning models in real world high-stakes decision settings continues to grow, it is highly important that we are able to audit and control for any potential…