David Madras

According to our database, David Madras authored at least 15 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Learning and Forgetting Unsafe Examples in Large Language Models.

Jiachen Zhao, Zhun Deng, David Madras, James Zou, Mengye Ren

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift.

Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard S. Zemel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2022

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data.

Sindy Löwe, David Madras, Richard S. Zemel, Max Welling

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

2021

Identifying and Benchmarking Natural Out-of-Context Prediction Problems.

David Madras, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification.

Robert Adragna, Elliot Creager, David Madras, Richard S. Zemel

CoRR, 2020

Causal Modeling for Fairness In Dynamical Systems.

Elliot Creager, David Madras, Toniann Pitassi, Richard S. Zemel

Proceedings of the 37th International Conference on Machine Learning, 2020

Detecting Extrapolation with Local Ensembles.

David Madras, James Atwood, Alexander D'Amour

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Flexibly Fair Representation Learning by Disentanglement.

Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel

Proceedings of the 36th International Conference on Machine Learning, 2019

Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data.

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019

2018

Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data.

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

CoRR, 2018

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer.

David Madras, Toniann Pitassi, Richard S. Zemel

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Adversarially Fair and Transferable Representations.

David Madras, Elliot Creager, Toniann Pitassi, Richard S. Zemel

Proceedings of the 35th International Conference on Machine Learning, 2018

Predict Responsibly: Increasing Fairness by Learning to Defer.

David Madras, Toniann Pitassi, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Change-point Detection Methods for Body-Worn Video.

Stephanie Allen, David Madras, Ye Ye, Greg Zanotti

CoRR, 2016