Change-point Detection Methods for Body-Worn Video. - Stephanie Allen; David Madras; Ye Ye; Greg Zanotti (CoRR, 2016)

Predict Responsibly: Increasing Fairness by Learning to Defer. - David Madras; Toniann Pitassi; Richard S. Zemel (ICLR (Workshop), 2018)

Learning Adversarially Fair and Transferable Representations. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (ICML, 2018)

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. - David Madras; Toniann Pitassi; Richard S. Zemel (NeurIPS, 2018)

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (CoRR, 2018)

Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. - David Madras; Elliot Creager; Toniann Pitassi; Richard S. Zemel (FAT, 2019)

Flexibly Fair Representation Learning by Disentanglement. - Elliot Creager; David Madras; Jörn-Henrik Jacobsen; Marissa A. Weis; Kevin Swersky; Toniann Pitassi; Richard S. Zemel (ICML, 2019)

Detecting Extrapolation with Local Ensembles. - David Madras; James Atwood; Alexander D'Amour (ICLR, 2020)

Causal Modeling for Fairness In Dynamical Systems. - Elliot Creager; David Madras; Toniann Pitassi; Richard S. Zemel (ICML, 2020)

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. - Robert Adragna; Elliot Creager; David Madras; Richard S. Zemel (CoRR, 2020)

Identifying and Benchmarking Natural Out-of-Context Prediction Problems. - David Madras; Richard S. Zemel (NeurIPS, 2021)

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. - Sindy Löwe; David Madras; Richard S. Zemel; Max Welling (CLeaR, 2022)

Towards Trustworthy Machine Learning in High-Stakes Decision-Making Systems - David Madras (2023)

Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. - Hansa Srinivasan; Candice Schumann; Aradhana Sinha; David Madras; Gbolahan Oluwafemi Olanubi; Alex Beutel; Susanna Ricco; Jilin Chen (FAccT, 2024)

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. - Benjamin Eyre; Elliot Creager; David Madras; Vardan Papyan; Richard S. Zemel (ICML, 2024)

Learning and Forgetting Unsafe Examples in Large Language Models. - Jiachen Zhao; Zhun Deng; David Madras; James Zou 001; Mengye Ren (ICML, 2024)

Auto-Evaluation with Few Labels through Post-hoc Regression. - Benjamin Eyre; David Madras (CoRR, 2024)

Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness. - Stephen R. Pfohl; Natalie Harris; Chirag Nagpal; David Madras; Vishwali Mhasawade; Olawale Salaudeen; Awa Dieng; Shannon Sequeira; Santiago Eduardo Arciniegas; Lillian Sung; Nnamdi Ezeanochie; Heather Cole-Lewis; et al. (CoRR, 2025)

QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. - Zhun Deng; Thomas P. Zollo; Benjamin Eyre; Amogh Inamdar; David Madras; Richard S. Zemel (CoRR, 2025)