Alexander D'Amour

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 45

Top Topics

Kolmogorov Smirnov

Privileged Information

Covariate Shift

Causal Inference

Top Venues

Publications

Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? CoRR (2024)
Jacy Reese Anthis, Kristian Lum, Michael D. Ekstrand, Avi Feller, Alexander D'Amour, Chenhao Tan
The Impossibility of Fair LLMs. CoRR (2024)
Ibrahim Alabdulmohsin, Xiao Wang, Andreas Peter Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai
CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? ICLR (2024)
Kristian Lum, Jacy Reese Anthis, Chirag Nagpal, Alexander D'Amour
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation. CoRR (2024)
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
Proxy Methods for Domain Adaptation. AISTATS (2024)
Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
Theoretical guarantees on the best-of-n alignment policy. CoRR (2024)
Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
Mind the Graph When Balancing Data for Fairness or Robustness. CoRR (2024)
Vishwali Mhasawade, Alexander D'Amour, Stephen R. Pfohl
A Causal Perspective on Label Bias. FAccT (2024)
Jamelle Watson-Daniels, Flávio du Pin Calmon, Alexander D'Amour, Carol Xuan Long, David C. Parkes, Berk Ustun
Predictive Churn with the Set of Good Models. CoRR (2024)
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
Proxy Methods for Domain Adaptation. CoRR (2024)
Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
Choosing a Proxy Metric from Past Experiments. KDD (2024)
Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
Choosing a Proxy Metric from Past Experiments. CoRR (2023)
Qingyao Sun, Kevin P. Murphy, Sayna Ebrahimi, Alexander D'Amour
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations. NeurIPS (2023)
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS (2023)
Harini Suresh, Divya Shanmugam, Tiffany Chen, Annie G. Bryan, Alexander D'Amour, John V. Guttag, Arvind Satyanarayan
Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. CHI (2023)
Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander D'Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
When does Privileged Information Explain Away Label Noise? CoRR (2023)
Maggie Makar, Alexander D'Amour
Fairness and robustness in anti-causal prediction. Trans. Mach. Learn. Res. 2023 (2023)
Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations. CoRR (2022)
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR (2022)
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
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. NeurIPS (2022)
Jessica Schrouff, Natalie Harris, Oluwasanmi Koyejo, Ibrahim Alabdulmohsin, Eva Schnider, Krista Opsahl-Ong, Alex Brown, Subhrajit Roy, Diana Mincu, Christina Chen, Awa Dieng, Yuan Liu, Vivek Natarajan, Alan Karthikesalingam, Katherine A. Heller, Silvia Chiappa, Alexander D'Amour
Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? CoRR (2022)
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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Mingjun Chen
Boosting the interpretability of clinical risk scores with intervention predictions. CoRR (2022)
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour
Causally motivated shortcut removal using auxiliary labels. AISTATS (2022)
Maggie Makar, Alexander D'Amour
Fairness and robustness in anti-causal prediction. CoRR (2022)
Jiajing Zheng, Alexander D'Amour, Alexander Franks
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. AISTATS (2022)
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
The MultiBERTs: BERT Reproductions for Robustness Analysis. ICLR (2022)
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, Alexander D'Amour
Causally-motivated Shortcut Removal Using Auxiliary Labels. CoRR (2021)
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS (2021)
Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. NeurIPS (2021)
Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. CoRR (2021)
Thibault Sellam, Steve Yadlowsky, Jason Wei, Naomi Saphra, Alexander D'Amour, Tal Linzen, Jasmijn Bastings, Iulia Turc, Jacob Eisenstein, Dipanjan Das, Ian Tenney, Ellie Pavlick
The MultiBERTs: BERT Reproductions for Robustness Analysis. CoRR (2021)
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
On Robustness and Transferability of Convolutional Neural Networks. CVPR (2021)
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR (2021)
Alexander D'Amour
Revisiting Rashomon: A Comment on "The Two Cultures". CoRR (2021)
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
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR (2020)
Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern
Fairness is not static: deeper understanding of long term fairness via simulation studies. FAT* (2020)
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
On Robustness and Transferability of Convolutional Neural Networks. CoRR (2020)
David Madras, James Atwood, Alexander D'Amour
Detecting Extrapolation with Local Ensembles. ICLR (2020)
Alexander D'Amour
On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives. CoRR (2019)
Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour
Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. CD@KDD (2019)
Niklas T. Rindtorff, Mingyu Lu, Nisarg A. Patel, Huahua Zheng, Alexander D'Amour
A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data. CoRR (2019)
Alexander D'Amour
On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative. AISTATS (2019)
Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams
Reducing Reparameterization Gradient Variance. NIPS (2017)