Reducing Reparameterization Gradient Variance. - Andrew C. Miller; Nick Foti; Alexander D'Amour; Ryan P. Adams (NIPS, 2017)

BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity. - Alexey A. Gritsenko; Alex D'Amour; James Atwood; Yoni Halpern; D. Sculley (CoRR, 2018)

On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative. - Alexander D'Amour (AISTATS, 2019)

Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. - Alexander Lin; Amil Merchant; Suproteem K. Sarkar; Alexander D'Amour (CD@KDD, 2019)

On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives. - Alexander D'Amour (CoRR, 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)

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 (FAT*, 2020)

Detecting Extrapolation with Local Ensembles. - David Madras; James Atwood; Alexander D'Amour (ICLR, 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)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

Counterfactual Invariance to Spurious Correlations in Text Classification. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (NeurIPS, 2021)

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. - Steve Yadlowsky; Taedong Yun; Cory Y. McLean; Alexander D'Amour (NeurIPS, 2021)

Revisiting Rashomon: A Comment on "The Two Cultures". - Alexander D'Amour (CoRR, 2021)

Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. - Victor Veitch; Alexander D'Amour; Steve Yadlowsky; Jacob Eisenstein (CoRR, 2021)

Causally motivated shortcut removal using auxiliary labels. - Maggie Makar; Ben Packer; Dan Moldovan; Davis W. Blalock; Yoni Halpern; Alexander D'Amour (AISTATS, 2022)

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. - Jiajing Zheng; Alexander D'Amour; Alexander Franks (AISTATS, 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 001; Ellie Pavlick (ICLR, 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; et al. (NeurIPS, 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; et al. (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)

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations. - Qingyao Sun; Kevin Murphy 002; Sayna Ebrahimi; Alexander D'Amour (CoRR, 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; et al. (J. Mach. Learn. Res., 2022)

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 (AISTATS, 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 (CHI, 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 (ICML, 2023)

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations. - Qingyao Sun; Kevin P. Murphy; Sayna Ebrahimi; Alexander D'Amour (NeurIPS, 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 004; Jonathan Berant (CoRR, 2023)

Fairness and robustness in anti-causal prediction. - Maggie Makar; Alexander D'Amour (Trans. Mach. Learn. Res., 2023)

Proxy Methods for Domain Adaptation. - Katherine Tsai; Stephen R. Pfohl; Olawale Salaudeen; Nicole Chiou; Matt J. Kusner; Alexander D'Amour; Sanmi Koyejo; Arthur Gretton (AISTATS, 2024)

A Causal Perspective on Label Bias. - Vishwali Mhasawade; Alexander D'Amour; Stephen R. Pfohl (FAccT, 2024)

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning? - Ibrahim Alabdulmohsin; Xiao Wang 038; Andreas Peter Steiner; Priya Goyal; Alexander D'Amour; Xiaohua Zhai (ICLR, 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 (ICML, 2024)

Choosing a Proxy Metric from Past Experiments. - Nilesh Tripuraneni; Lee Richardson; Alexander D'Amour; Jacopo Soriano; Steve Yadlowsky (KDD, 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 (NeurIPS, 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)

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)

Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation. - Kristian Lum; Jacy Reese Anthis; Chirag Nagpal; Alexander D'Amour (CoRR, 2024)

The Impossibility of Fair LLMs. - Jacy Reese Anthis; Kristian Lum; Michael D. Ekstrand; Avi Feller; Alexander D'Amour; Chenhao Tan (CoRR, 2024)

When Can Proxies Improve the Sample Complexity of Preference Learning? - Yuchen Zhu; Daniel Augusto de Souza; Zhengyan Shi; Mengyue Yang; Pasquale Minervini; Alexander D'Amour; Matt J. Kusner (CoRR, 2024)

Bias in Language Models: Beyond Trick Tests and Towards RUTEd Evaluation. - Kristian Lum; Jacy Reese Anthis; Kevin Robinson; Chirag Nagpal; Alexander Nicholas D'Amour (ACL (1), 2025)

Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness. - 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; et al. (CoRR, 2025)