9 citations · 14 across the 4 of their papers we have counts for
4 papers
Algorithmic Arbitrariness in Content Moderation
Juan Felipe Gomez, Caio Vieira Machado, Lucas Monteiro Paes +1
Machine learning (ML) is widely used to moderate online content. Despite its scalability relative to human moderation, the use of ML introduces unique challenges to content moderat…
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…
The Saddle-Point Accountant for Differential Privacy
Wael Alghamdi, Shahab Asoodeh, Flavio P. Calmon +4
We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA). SPA approximates privacy guarantees for the composition of DP mechanisms in an acc…