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08/24/2023
Prediction without Preclusion: Recourse Verification with Reachable Sets
Machine learning models are often used to decide who will receive a loan...
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02/28/2023
Arbitrary Decisions are a Hidden Cost of Differentially-Private Training
Mechanisms used in privacy-preserving machine learning often aim to guar...
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08/27/2022
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
Many machine learning problems use data in the tabular domains. Adversar...
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04/07/2022
What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning
We investigate and leverage a connection between Differential Privacy (D...
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07/11/2021
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
Attacks from adversarial machine learning (ML) have the potential to be ...
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07/05/2021
Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision
Researchers have identified datasets used for training computer vision (...
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11/06/2019
zksk: A Library for Composable Zero-Knowledge Proofs
Zero-knowledge proofs are an essential building block in many privacy-pr...
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06/02/2019
Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning
A membership inference attack (MIA) against a machine learning model ena...
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11/27/2018
Questioning the assumptions behind fairness solutions
In addition to their benefits, optimization systems can have negative ec...
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10/25/2018
Evading classifiers in discrete domains with provable optimality guarantees
Security-critical applications such as malware, fraud, or spam detection...
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06/07/2018
POTs: Protective Optimization Technologies
In spite of their many advantages, optimization systems often neglect th...
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11/14/2017
