17 citations · 27 across the 5 of their papers we have counts for
5 papers · 1 filter
Feedback in Imitation Learning: The Three Regimes of Covariate Shift
Jonathan Spencer, Sanjiban Choudhury, Arun Venkatraman +2
Imitation learning practitioners have often noted that conditioning policies on previous actions leads to a dramatic divergence between "held out" error and performance of the lear…
Robust Fairness under Covariate Shift
Ashkan Rezaei, Anqi Liu, Omid Memarrast +1
Making predictions that are fair with regard to protected group membership (race, gender, age, etc.) has become an important requirement for classification algorithms. Existing tec…
Fairness for Robust Log Loss Classification
Ashkan Rezaei, Rizal Fathony, Omid Memarrast +1
Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in socia…
Kernel Robust Bias-Aware Prediction under Covariate Shift
Anqi Liu, Rizal Fathony, Brian D. Ziebart
Under covariate shift, training (source) data and testing (target) data differ in input space distribution, but share the same conditional label distribution. This poses a challeng…
Robust Covariate Shift Prediction with General Losses and Feature Views
Anqi Liu, Brian D. Ziebart
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions. Unfortunately,…