14 citations · 48 across the 7 of their papers we have counts for
6 papers · 1 filter
Outcome Indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold +2
Prediction algorithms assign numbers to individuals that are popularly understood as individual "probabilities" -- what is the probability of 5-year survival after cancer diagnosis…
Interpreting Robust Optimization via Adversarial Influence Functions
Zhun Deng, Cynthia Dwork, Jialiang Wang +1
Robust optimization has been widely used in nowadays data science, especially in adversarial training. However, little research has been done to quantify how robust optimization ch…
Private Post-GAN Boosting
Marcel Neunhoeffer, Zhiwei Steven Wu, Cynthia Dwork
Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the privacy of individuals. Due to the privacy-prote…
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
Zhun Deng, Frances Ding, Cynthia Dwork +4
We investigate the power of censoring techniques, first developed for learning {\em fair representations}, to address domain generalization. We examine {\em adversarial} censoring…
Individual Fairness in Pipelines
Cynthia Dwork, Christina Ilvento, Meena Jagadeesan
It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline com…
Abstracting Fairness: Oracles, Metrics, and Interpretability
Cynthia Dwork, Christina Ilvento, Guy N. Rothblum +1
It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We exami…