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What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning., , , , and . CoRR, (2022)Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision., , and . CoRR, (2021)Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks., , , and . CoRR, (2021)The Fundamental Limits of Least-Privilege Learning., , , , and . CoRR, (2024)Disparate Vulnerability to Membership Inference Attacks., , , , and . Proc. Priv. Enhancing Technol., 2022 (1): 460-480 (2022)Questioning the assumptions behind fairness solutions., , , , and . CoRR, (2018)What You See is What You Get: Principled Deep Learning via Distributional Generalization., , , , and . NeurIPS, (2022)POTs: protective optimization technologies., , , and . FAT*, page 177-188. ACM, (2020)zksk: A Library for Composable Zero-Knowledge Proofs., , , , and . WPES@CCS, page 50-54. ACM, (2019)Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning., , and . CoRR, (2019)