352 citations · 1k across the 42 of their papers we have counts for
6 papers · 1 filter
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization
Valerio Perrone, Huibin Shen, Aida Zolic +12
Tuning complex machine learning systems is challenging. Machine learning typically requires to set hyperparameters, be it regularization, architecture, or optimization parameters,…
Minimax Group Fairness: Algorithms and Experiments
Emily Diana, Wesley Gill, Michael Kearns +2
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.…
LiFT: A Scalable Framework for Measuring Fairness in ML Applications
Sriram Vasudevan, Krishnaram Kenthapadi
Many internet applications are powered by machine learned models, which are usually trained on labeled datasets obtained through either implicit / explicit user feedback signals or…
Evaluating Fairness Using Permutation Tests
Cyrus DiCiccio, Sriram Vasudevan, Kinjal Basu +2
Machine learning models are central to people's lives and impact society in ways as fundamental as determining how people access information. The gravity of these models imparts a…
Fairness-Aware Online Personalization
G Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluenc…
Fair Bayesian Optimization
Valerio Perrone, Michele Donini, Muhammad Bilal Zafar +3
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML model…