511 citations · 968 across the 39 of their papers we have counts for
8 papers · 1 filter
Outlier-Robust Optimal Transport
Debarghya Mukherjee, Aritra Guha, Justin Solomon +2
Optimal transport (OT) measures distances between distributions in a way that depends on the geometry of the sample space. In light of recent advances in computational OT, OT dista…
Matrix Completion Methods for the Total Electron Content Video Reconstruction
Hu Sun, Zhijun Hua, Jiaen Ren +3
The total electron content (TEC) maps can be used to estimate the signal delay of GPS due to the ionospheric electron content between a receiver and satellite. This delay can resul…
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training da…
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee +1
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific f…
SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
Mikhail Yurochkin, Yuekai Sun
In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets.…
Auditing ML Models for Individual Bias and Unfairness
Songkai Xue, Mikhail Yurochkin, Yuekai Sun
We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal…
Minimax optimal approaches to the label shift problem in non-parametric settings
Subha Maity, Yuekai Sun, Moulinath Banerjee
We study the minimax rates of the label shift problem in non-parametric classification. In addition to the unsupervised setting in which the learner only has access to unlabeled ex…
Federated Learning with Matched Averaging
Hongyi Wang, Mikhail Yurochkin, Yuekai Sun +2
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need…