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2020 – today
- 2024
[c21]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: 3961-3969- [c20]Vishwali Mhasawade
, Alexander D'Amour, Stephen R. Pfohl:
A Causal Perspective on Label Bias. FAccT 2024: 1282-1294 - [c19]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 - [c18]Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alexander Nicholas D'Amour, Sanmi Koyejo, Victor Veitch:
Transforming and Combining Rewards for Aligning Large Language Models. ICML 2024 - [c17]Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky:
Choosing a Proxy Metric from Past Experiments. KDD 2024: 5803-5812 - [i28]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 abs/2401.01879 (2024) - [i27]Zihao Wang, Chirag Nagpal, Jonathan Berant, Jacob Eisenstein, Alex D'Amour, Sanmi Koyejo, Victor Veitch:
Transforming and Combining Rewards for Aligning Large Language Models. CoRR abs/2402.00742 (2024) - [i26]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 abs/2402.07745 (2024) - [i25]Kristian Lum, Jacy Reese Anthis, Chirag Nagpal, Alexander D'Amour:
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation. CoRR abs/2402.12649 (2024) - [i24]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 abs/2403.04547 (2024) - [i23]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 abs/2403.07442 (2024) - [i22]Jacy Reese Anthis, Kristian Lum, Michael D. Ekstrand, Avi Feller, Alexander D'Amour, Chenhao Tan:
The Impossibility of Fair LLMs. CoRR abs/2406.03198 (2024) - [i21]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 abs/2406.17433 (2024) - 2023
- [j2]Maggie Makar, Alexander D'Amour:
Fairness and robustness in anti-causal prediction. Trans. Mach. Learn. Res. 2023 (2023) - [c16]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: 9637-9661 - [c15]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: 775:1-775:13 - [c14]Guillermo Ortiz-Jiménez, Mark Collier, Anant Nawalgaria, Alexander Nicholas D'Amour, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou:
When does Privileged information Explain Away Label Noise? ICML 2023: 26646-26669 - [c13]Qingyao Sun, Kevin P. Murphy, Sayna Ebrahimi, Alexander D'Amour:
Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations. NeurIPS 2023 - [i20]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 abs/2303.01806 (2023) - [i19]Nilesh Tripuraneni, Lee Richardson, Alexander D'Amour, Jacopo Soriano, Steve Yadlowsky:
Choosing a Proxy Metric from Past Experiments. CoRR abs/2309.07893 (2023) - [i18]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:
Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking. CoRR abs/2312.09244 (2023) - 2022
- [j1]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: 226:1-226:61 (2022) - [c12]Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour:
Causally motivated shortcut removal using auxiliary labels. AISTATS 2022: 739-766 - [c11]Jiajing Zheng, Alexander D'Amour, Alexander Franks:
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding. AISTATS 2022: 3608-3626 - [c10]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 - [c9]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 - [i17]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:
Maintaining fairness across distribution shift: do we have viable solutions for real-world applications? CoRR abs/2202.01034 (2022) - [i16]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 abs/2207.02941 (2022) - [i15]Maggie Makar, Alexander D'Amour:
Fairness and robustness in anti-causal prediction. CoRR abs/2209.09423 (2022) - [i14]Qingyao Sun, Kevin Murphy, Sayna Ebrahimi, Alexander D'Amour:
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations. CoRR abs/2211.15646 (2022) - [i13]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 abs/2212.11254 (2022) - 2021
- [c8]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: 16458-16468 - [c7]Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein:
Counterfactual Invariance to Spurious Correlations in Text Classification. NeurIPS 2021: 16196-16208 - [c6]Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour:
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. NeurIPS 2021: 29517-29528 - [i12]Steve Yadlowsky, Taedong Yun, Cory Y. McLean, Alexander D'Amour:
SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression. CoRR abs/2103.12725 (2021) - [i11]Alexander D'Amour:
Revisiting Rashomon: A Comment on "The Two Cultures". CoRR abs/2104.02150 (2021) - [i10]Maggie Makar, Ben Packer, Dan Moldovan, Davis W. Blalock, Yoni Halpern, Alexander D'Amour:
Causally-motivated Shortcut Removal Using Auxiliary Labels. CoRR abs/2105.06422 (2021) - [i9]Victor Veitch, Alexander D'Amour, Steve Yadlowsky, Jacob Eisenstein:
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests. CoRR abs/2106.00545 (2021) - [i8]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 abs/2106.16163 (2021) - 2020
- [c5]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: 525-534 - [c4]David Madras, James Atwood, Alexander D'Amour:
Detecting Extrapolation with Local Ensembles. ICLR 2020 - [i7]Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek:
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR abs/2006.10963 (2020) - [i6]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 abs/2007.08558 (2020) - [i5]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 abs/2011.03395 (2020)
2010 – 2019
- 2019
- [c3]Alexander D'Amour:
On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative. AISTATS 2019: 3478-3486 - [c2]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: 50-58 - [i4]Alexander D'Amour:
On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives. CoRR abs/1902.10286 (2019) - [i3]David Madras, James Atwood, Alex D'Amour:
Detecting Extrapolation with Local Ensembles. CoRR abs/1910.09573 (2019) - [i2]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 abs/1911.04389 (2019) - 2018
- [i1]Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley:
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity. CoRR abs/1812.06869 (2018) - 2017
- [c1]Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams:
Reducing Reparameterization Gradient Variance. NIPS 2017: 3708-3718
Coauthor Index
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