David Madras

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Covariate Shift

Top Venues

Publications

Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR (2024)
Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. FAccT (2024)
Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
Learning and Forgetting Unsafe Examples in Large Language Models. CoRR (2023)
Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR (2023)
Sindy Löwe, David Madras, Richard Z. Shilling, Max Welling
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CLeaR (2022)
David Madras, Richard S. Zemel
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. NeurIPS (2021)
David Madras, Richard S. Zemel
Identifying and Benchmarking Natural Out-of-Context Prediction Problems. CoRR (2021)
Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
Causal Modeling for Fairness In Dynamical Systems. ICML (2020)
Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR (2020)
Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR (2020)
David Madras, James Atwood, Alexander D'Amour
Detecting Extrapolation with Local Ensembles. ICLR (2020)
David Madras, James Atwood, Alex D'Amour
Detecting Extrapolation with Local Ensembles. CoRR (2019)
David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. ICML (2019)
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
Flexibly Fair Representation Learning by Disentanglement. CoRR (2019)
Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
Causal Modeling for Fairness in Dynamical Systems. CoRR (2019)
David Madras, Toniann Pitassi, Richard S. Zemel
Predict Responsibly: Increasing Fairness by Learning to Defer. ICLR (Workshop) (2018)
David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
Learning Adversarially Fair and Transferable Representations. ICML (2018)
David Madras, Toniann Pitassi, Richard S. Zemel
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. NeurIPS (2018)
David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
Learning Adversarially Fair and Transferable Representations. CoRR (2018)
David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. CoRR (2018)
David Madras, Toniann Pitassi, Richard S. Zemel
Predict Responsibly: Increasing Fairness by Learning To Defer. CoRR (2017)
Stephanie Allen, David Madras, Ye Ye, Greg Zanotti
Change-point Detection Methods for Body-Worn Video. CoRR (2016)