Madras, David

14 publications

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

ICML 2025 Regression for the Mean: Auto-Evaluation and Inference with Few Labels Through Post-Hoc Regression Benjamin Eyre, David Madras

NeurIPS 2025 Understanding Challenges to the Interpretation of Disaggregated Evaluations of Algorithmic Fairness Stephen R Pfohl, Natalie Harris, Chirag Nagpal, David Madras, Vishwali Mhasawade, Olawale Elijah Salaudeen, Awa Dieng, Shannon Sequeira, Santiago Eduardo Arciniegas, Lillian Sung, Nnamdi Peter Okechukwu Ezeanochie, Heather Cole-Lewis, Katherine A Heller, Sanmi Koyejo, Alexander Nicholas D'Amour

ICML 2024 Learning and Forgetting Unsafe Examples in Large Language Models Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren

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

NeurIPSW 2023 Understanding Subgroup Performance Differences of Fair Predictors Using Causal Models Stephen Robert Pfohl, Natalie Harris, Chirag Nagpal, David Madras, Vishwali Mhasawade, Olawale Elijah Salaudeen, Katherine A Heller, Sanmi Koyejo, Alexander Nicholas D'Amour

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

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

NeurIPSW 2021 Understanding Post-Hoc Adaptation for Improving Subgroup Robustness David Madras, Richard Zemel

ICML 2020 Causal Modeling for Fairness in Dynamical Systems Elliot Creager, David Madras, Toniann Pitassi, Richard Zemel

ICLR 2020 Detecting Extrapolation with Local Ensembles David Madras, James Atwood, Alex D'Amour

ICML 2019 Flexibly Fair Representation Learning by Disentanglement Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

ICML 2018 Learning Adversarially Fair and Transferable Representations David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel

NeurIPS 2018 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer David Madras, Toni Pitassi, Richard Zemel