David Madras - RankMe

32 papers A* 10Journal 17Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Prompts Generalize with Low Data: Non-vacuous Generalization Bounds for Optimizing Prompts with More Informative Priors. CoRR David Madras, Joshua Safyan, Qiuyi Zhang
2025	A*	`conf`	QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. ICML Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel
2025	J	`jnl`	QuEst: Enhancing Estimates of Quantile-Based Distributional Measures Using Model Predictions. CoRR Zhun Deng, Thomas P. Zollo, Benjamin Eyre, Amogh Inamdar, David Madras, Richard S. Zemel
2025	A*	`conf`	Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression. ICML Benjamin Eyre, David Madras
2025	J	`jnl`	Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness. CoRR 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, Katherine A. Heller, Sanmi Koyejo, Alexander D'Amour
2024	J	`jnl`	Auto-Evaluation with Few Labels through Post-hoc Regression. CoRR Benjamin Eyre, David Madras
2024	—	`conf`	Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. FAccT Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
2024	J	`jnl`	Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen
2024	A*	`conf`	Learning and Forgetting Unsafe Examples in Large Language Models. ICML Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
2024	A*	`conf`	Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. ICML Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
2023	J	`jnl`	Learning and Forgetting Unsafe Examples in Large Language Models. CoRR Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren
2023	J	`jnl`	Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift. CoRR Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel
2023	—	`—`	Towards Trustworthy Machine Learning in High-Stakes Decision-Making Systems David Madras
2022	—	`conf`	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CLeaR Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
2021	A*	`conf`	Identifying and Benchmarking Natural Out-of-Context Prediction Problems. NeurIPS David Madras, Richard S. Zemel
2021	J	`jnl`	Identifying and Benchmarking Natural Out-of-Context Prediction Problems. CoRR David Madras, Richard S. Zemel
2020	J	`jnl`	Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. CoRR Sindy Löwe, David Madras, Richard S. Zemel, Max Welling
2020	A*	`conf`	Causal Modeling for Fairness In Dynamical Systems. ICML Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2020	A*	`conf`	Detecting Extrapolation with Local Ensembles. ICLR David Madras, James Atwood, Alexander D'Amour
2020	J	`jnl`	Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification. CoRR Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel
2019	J	`jnl`	Causal Modeling for Fairness in Dynamical Systems. CoRR Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Detecting Extrapolation with Local Ensembles. CoRR David Madras, James Atwood, Alex D'Amour
2019	—	`conf`	Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data. FAT David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2019	A*	`conf`	Flexibly Fair Representation Learning by Disentanglement. ICML Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2019	J	`jnl`	Flexibly Fair Representation Learning by Disentanglement. CoRR Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel
2018	J	`jnl`	Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data. CoRR David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	A*	`conf`	Learning Adversarially Fair and Transferable Representations. ICML David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	J	`jnl`	Learning Adversarially Fair and Transferable Representations. CoRR David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel
2018	A*	`conf`	Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer. NeurIPS David Madras, Toniann Pitassi, Richard S. Zemel
2018	—	`conf`	Predict Responsibly: Increasing Fairness by Learning to Defer. ICLR (Workshop) David Madras, Toniann Pitassi, Richard S. Zemel
2017	J	`jnl`	Predict Responsibly: Increasing Fairness by Learning To Defer. CoRR David Madras, Toniann Pitassi, Richard S. Zemel
2016	J	`jnl`	Change-point Detection Methods for Body-Worn Video. CoRR Stephanie Allen, David Madras, Ye Ye, Greg Zanotti