Alexander D'Amour

49 papers A* 11A 5Journal 29Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding. J. Mach. Learn. Res. Jiajing Zheng, Alexander D'Amour, Alexander Franks
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	—	`conf`	A Causal Perspective on Label Bias. FAccT Vishwali Mhasawade, Alexander D'Amour, Stephen R. Pfohl
2024	J	`jnl`	Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation. CoRR Kristian Lum, Jacy Reese Anthis, Chirag Nagpal, Alexander D'Amour
2024	A*	`conf`	CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? ICLR Ibrahim Alabdulmohsin, Xiao Wang, Andreas Peter Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai
2024	J	`jnl`	CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? CoRR Ibrahim Alabdulmohsin, Xiao Wang, Andreas Steiner, Priya Goyal, Alexander D'Amour, Xiaohua Zhai
2024	A*	`conf`	Choosing a Proxy Metric from Past Experiments. KDD Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
2024	A*	`conf`	Mind the Graph When Balancing Data for Fairness or Robustness. NeurIPS Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
2024	J	`jnl`	Mind the Graph When Balancing Data for Fairness or Robustness. CoRR Jessica Schrouff, Alexis Bellot, Amal Rannen-Triki, Alan Malek, Isabela Albuquerque, Arthur Gretton, Alexander D'Amour, Silvia Chiappa
2024	J	`jnl`	Predictive Churn with the Set of Good Models. CoRR Jamelle Watson-Daniels, Flávio du Pin Calmon, Alexander D'Amour, Carol Xuan Long, David C. Parkes, Berk Ustun
2024	A	`conf`	Proxy Methods for Domain Adaptation. AISTATS Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	Proxy Methods for Domain Adaptation. CoRR Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024	J	`jnl`	The Impossibility of Fair LLMs. CoRR Jacy Reese Anthis, Kristian Lum, Michael D. Ekstrand, Avi Feller, Alexander D'Amour, Chenhao Tan
2024	J	`jnl`	Theoretical guarantees on the best-of-n alignment policy. CoRR Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D'Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
2024	J	`jnl`	When Can Proxies Improve the Sample Complexity of Preference Learning? CoRR Yuchen Zhu, Daniel Augusto de Souza, Zhengyan Shi, Mengyue Yang, Pasquale Minervini, Alexander D'Amour, Matt J. Kusner
2023	A	`conf`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. AISTATS Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2023	A*	`conf`	Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations. NeurIPS Qingyao Sun, Kevin P. Murphy, Sayna Ebrahimi, Alexander D'Amour
2023	J	`jnl`	Choosing a Proxy Metric from Past Experiments. CoRR Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky
2023	J	`jnl`	Fairness and robustness in anti-causal prediction. Trans. Mach. Learn. Res. Maggie Makar, Alexander D'Amour
2023	A*	`conf`	Kaleidoscope: Semantically-grounded, context-specific ML model evaluation. CHI Harini Suresh, Divya Shanmugam, Tiffany L. Chen, Annie G. Bryan, Alexander D'Amour, John V. Guttag, Arvind Satyanarayan
2023	J	`jnl`	When does Privileged Information Explain Away Label Noise? CoRR Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander D'Amour, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou
2022	J	`jnl`	Adapting to Latent Subgroup Shifts via Concepts and Proxies. CoRR Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
2022	A	`conf`	Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. AISTATS Jiajing Zheng, Alexander D'Amour, Alexander Franks
2022	J	`jnl`	Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations. CoRR Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour
2022	J	`jnl`	Boosting the interpretability of clinical risk scores with intervention predictions. CoRR Eric Loreaux, Ke Yu, Jonas Kemp, Martin Seneviratne, Christina Chen, Subhrajit Roy, Ivan Protsyuk, Natalie Harris, Alexander D'Amour, Steve Yadlowsky, Mingjun Chen
2022	A	`conf`	Causally motivated shortcut removal using auxiliary labels. AISTATS Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour
2022	A*	`conf`	Diagnosing failures of fairness transfer across distribution shift in real-world medical settings. NeurIPS 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
2022	J	`jnl`	Fairness and robustness in anti-causal prediction. CoRR Maggie Makar, Alexander D'Amour
2022	J	`jnl`	Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? CoRR 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
2022	A*	`conf`	The MultiBERTs: BERT Reproductions for Robustness Analysis. ICLR 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
2022	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 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
2021	J	`jnl`	Causally-motivated Shortcut Removal Using Auxiliary Labels. CoRR Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour
2021	A*	`conf`	Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
2021	J	`jnl`	Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein
2021	A*	`conf`	On Robustness and Transferability of Convolutional Neural Networks. CVPR 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
2021	J	`jnl`	Revisiting Rashomon: A Comment on "The Two Cultures". CoRR Alexander D'Amour
2021	A*	`conf`	SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. NeurIPS Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour
2021	J	`jnl`	SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. CoRR Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour
2021	J	`jnl`	The MultiBERTs: BERT Reproductions for Robustness Analysis. CoRR 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
2020	A*	`conf`	Detecting Extrapolation with Local Ensembles. ICLR David Madras, James Atwood, Alexander D'Amour
2020	J	`jnl`	Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
2020	—	`conf`	Fairness is not static: deeper understanding of long term fairness via simulation studies. FAT* Alexander D'Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, D. Sculley, Yoni Halpern
2020	J	`jnl`	On Robustness and Transferability of Convolutional Neural Networks. CoRR 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
2020	J	`jnl`	Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR 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
2019	J	`jnl`	A Biologically Plausible Benchmark for Contextual Bandit Algorithms in Precision Oncology Using in vitro Data. CoRR Niklas T. Rindtorff, Mingyu Lu, Nisarg A. Patel, Huahua Zheng, Alexander D'Amour
2019	A	`conf`	On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative. AISTATS Alexander D'Amour
2019	J	`jnl`	On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives. CoRR Alexander D'Amour
2019	—	`conf`	Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. CD@KDD Alexander Lin, Amil Merchant, Suproteem K. Sarkar, Alexander D'Amour
2017	—	`conf`	Reducing Reparameterization Gradient Variance. NIPS Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams