The Unintended Consequences of Overfitting: Training Data Inference Attacks. - Samuel Yeom; Matt Fredrikson; Somesh Jha (CoRR, 2017)

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. - Samuel Yeom; Irene Giacomelli; Matt Fredrikson; Somesh Jha (CSF, 2018)

Hunting for Discriminatory Proxies in Linear Regression Models. - Samuel Yeom; Anupam Datta; Matt Fredrikson (NeurIPS, 2018)

Discriminative but Not Discriminatory: A Comparison of Fairness Definitions under Different Worldviews. - Samuel Yeom; Michael Carl Tschantz (CoRR, 2018)

FlipTest: Fairness Auditing via Optimal Transport. - Emily Black; Samuel Yeom; Matt Fredrikson (CoRR, 2019)

Learning Fair Representations for Kernel Models. - Zilong Tan; Samuel Yeom; Matt Fredrikson; Ameet Talwalkar (AISTATS, 2020)

FlipTest: fairness testing via optimal transport. - Emily Black; Samuel Yeom; Matt Fredrikson (FAT*, 2020)

Individual Fairness Revisited: Transferring Techniques from Adversarial Robustness. - Samuel Yeom; Matt Fredrikson (IJCAI, 2020)

Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning. - Samuel Yeom; Irene Giacomelli; Alan Menaged; Matt Fredrikson; Somesh Jha (J. Comput. Secur., 2020)

Avoiding Disparity Amplification under Different Worldviews. - Samuel Yeom; Michael Carl Tschantz (FAccT, 2021)

Black-Box Approaches to Fair Machine Learning. - Samuel Yeom (2021)

Black-Box Audits for Group Distribution Shifts. - Marc Juarez; Samuel Yeom; Matt Fredrikson (CoRR, 2022)