70 citations · 293 across the 70 of their papers we have counts for
8 papers · 1 filter
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel +2
Machine learning (ML) models used in prediction and classification tasks may display performance disparities across population groups determined by sensitive attributes (e.g., race…
Differentially Private Secure Multiplication: Hiding Information in the Rubble of Noise
Viveck R. Cadambe, Ateet Devulapalli, Haewon Jeong +1
We consider the problem of private distributed multi-party multiplication. It is well-established that Shamir secret-sharing coding strategies can enable perfect information-theore…
Fair Machine Unlearning: Data Removal while Mitigating Disparities
Alex Oesterling, Jiaqi Ma, Flavio P. Calmon +1
The Right to be Forgotten is a core principle outlined by regulatory frameworks such as the EU's General Data Protection Regulation (GDPR). This principle allows individuals to req…
Arbitrariness Lies Beyond the Fairness-Accuracy Frontier
Carol Xuan Long, Hsiang Hsu, Wael Alghamdi +1
Machine learning tasks may admit multiple competing models that achieve similar performance yet produce conflicting outputs for individual samples -- a phenomenon known as predicti…
Adapting Fairness Interventions to Missing Values
Raymond Feng, Flavio P. Calmon, Hao Wang
Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the s…
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization
Haitong Ma, Tianpeng Zhang, Yixuan Wu +2
We study the multi-agent Bayesian optimization (BO) problem, where multiple agents maximize a black-box function via iterative queries. We focus on Entropy Search (ES), a sample-ef…
Arbitrary Decisions are a Hidden Cost of Differentially Private Training
Bogdan Kulynych, Hsiang Hsu, Carmela Troncoso +1
Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomizat…
Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions
Hao Wang, Luxi He, Rui Gao +1
Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of disc…