26 citations · 32 across the 4 of their papers we have counts for
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stat.ML2021★ 3 cited
Individually Fair Ranking
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin +1
We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from major…
stat.ML2020
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often refle…
stat.ML2019
Training individually fair ML models with Sensitive Subspace Robustness
Mikhail Yurochkin, Amanda Bower, Yuekai Sun
We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the pe…