219 citations · 513 across the 53 of their papers we have counts for
10 papers · 1 filter
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification
James Atwood, Tina Tian, Ben Packer +5
Despite the rich literature on machine learning fairness, relatively little attention has been paid to remediating complex systems, where the final prediction is the combination of…
Information-Theoretic Bayes Risk Lower Bounds for Realizable Models
Matthew Nokleby, Ahmad Beirami
We derive information-theoretic lower bounds on the Bayes risk and generalization error of realizable machine learning models. In particular, we employ an analysis in which the rat…
Federated Learning with Heterogeneous Differential Privacy
Nasser Aldaghri, Hessam Mahdavifar, Ahmad Beirami
Federated learning (FL) takes a first step towards privacy-preserving machine learning by training models while keeping client data local. Models trained using FL may still leak pr…
Robustness through Data Augmentation Loss Consistency
Tianjian Huang, Shaunak Halbe, Chinnadhurai Sankar +5
While deep learning through empirical risk minimization (ERM) has succeeded at achieving human-level performance at a variety of complex tasks, ERM is not robust to distribution sh…
On Tilted Losses in Machine Learning: Theory and Applications
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite i…
A Stochastic Optimization Framework for Fair Risk Minimization
Andrew Lowy, Sina Baharlouei, Rakesh Pavan +2
Despite the success of large-scale empirical risk minimization (ERM) at achieving high accuracy across a variety of machine learning tasks, fair ERM is hindered by the incompatibil…