Alexander D'Amour

According to our database, Alexander D'Amour authored at least 38 papers between 2017 and 2024.

Collaborative distances:

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

2024

Mind the Graph When Balancing Data for Fairness or Robustness.

Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa

CoRR, 2024

The Impossibility of Fair LLMs.

Jacy Reese Anthis, Kristian Lum, Michael D. Ekstrand, Avi Feller, Alexander D'Amour, Chenhao Tan

CoRR, 2024

Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation.

Kristian Lum, Jacy Reese Anthis, Chirag Nagpal, Alexander D'Amour

CoRR, 2024

Predictive Churn with the Set of Good Models.

Jamelle Watson-Daniels, Flávio du Pin Calmon, Alexander D'Amour, Carol Xuan Long, David C. Parkes, Berk Ustun

CoRR, 2024

Theoretical guarantees on the best-of-n alignment policy.

Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh

CoRR, 2024

Choosing a Proxy Metric from Past Experiments.

Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Transforming and Combining Rewards for Aligning Large Language Models.

Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D'Amour, Sanmi Koyejo, Victor Veitch

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

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Ibrahim Alabdulmohsin, Xiao Wang, Andreas Peter Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Causal Perspective on Label Bias.

Vishwali Mhasawade, Alexander D'Amour, Stephen R. Pfohl

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

Proxy Methods for Domain Adaptation.

Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Fairness and robustness in anti-causal prediction.

Maggie Makar, Alexander D'Amour

Trans. Mach. Learn. Res., 2023

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.

Jacob Eisenstein, Chirag Nagpal, Alekh Agarwal, Ahmad Beirami, Alex D'Amour, Dj Dvijotham, Adam Fisch, Katherine A. Heller, Stephen Pfohl, Deepak Ramachandran, Peter Shaw, Jonathan Berant

CoRR, 2023

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations.

Qingyao Sun, Kevin P. Murphy, Sayna Ebrahimi, Alexander D'Amour

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

When does Privileged information Explain Away Label Noise?

Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D'Amour, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou

Proceedings of the International Conference on Machine Learning, 2023

Kaleidoscope: Semantically-grounded, context-specific ML model evaluation.

Harini Suresh, Divya Shanmugam, Tiffany Chen, Annie G. Bryan, Alexander D'Amour, John V. Guttag, Arvind Satyanarayan

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

Adapting to Latent Subgroup Shifts via Concepts and Proxies.

Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley

J. Mach. Learn. Res., 2022

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations.

Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour

CoRR, 2022

Boosting the interpretability of clinical risk scores with intervention predictions.

Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Mingjun Chen

CoRR, 2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?

Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo, Ibrahim Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour

CoRR, 2022

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings.

Jessica Schrouff, Natalie Harris, Sanmi Koyejo, Ibrahim M. Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alexander Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The MultiBERTs: BERT Reproductions for Robustness Analysis.

Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D'Amour, Tal Linzen, Jasmijn Bastings, Iulia Raluca Turc, Jacob Eisenstein, Dipanjan Das, Ellie Pavlick

Proceedings of the Tenth International Conference on Learning Representations, 2022

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding.

Jiajing Zheng, Alexander D'Amour, Alexander Franks

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Causally motivated shortcut removal using auxiliary labels.

Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests.

Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

CoRR, 2021

Revisiting Rashomon: A Comment on "The Two Cultures".

Alexander D'Amour

CoRR, 2021

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour

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

Counterfactual Invariance to Spurious Correlations in Text Classification.

Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein

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

On Robustness and Transferability of Convolutional Neural Networks.

Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek

CoRR, 2020

Detecting Extrapolation with Local Ensembles.

David Madras, James Atwood, Alexander D'Amour

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

Fairness is not static: deeper understanding of long term fairness via simulation studies.

Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern

Proceedings of the FAT* '20: Conference on Fairness, 2020

2019

A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data.

Niklas T. Rindtorff, Mingyu Lu, Nisarg A. Patel, Huahua Zheng, Alexander D'Amour

CoRR, 2019

On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives.

Alexander D'Amour

CoRR, 2019

Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models.

Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour

Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, 2019

On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative.

Alexander D'Amour

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity.

Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley

CoRR, 2018

2017

Reducing Reparameterization Gradient Variance.

Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017