Alexander D'Amour

Orcid: 0000-0001-7984-3366

According to our database1, Alexander D'Amour authored at least 38 papers between 2017 and 2024.

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Bibliography

2024
Mind the Graph When Balancing Data for Fairness or Robustness.
CoRR, 2024

The Impossibility of Fair LLMs.
CoRR, 2024

Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation.
CoRR, 2024

Predictive Churn with the Set of Good Models.
CoRR, 2024

Theoretical guarantees on the best-of-n alignment policy.
CoRR, 2024

Choosing a Proxy Metric from Past Experiments.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Transforming and Combining Rewards for Aligning Large Language Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?
Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Causal Perspective on Label Bias.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

Proxy Methods for Domain Adaptation.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Fairness and robustness in anti-causal prediction.
Trans. Mach. Learn. Res., 2023

Helping or Herding? Reward Model Ensembles Mitigate but do not Eliminate Reward Hacking.
CoRR, 2023

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing "Spurious" Correlations.
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?
Proceedings of the International Conference on Machine Learning, 2023

Kaleidoscope: Semantically-grounded, context-specific ML model evaluation.
Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

Adapting to Latent Subgroup Shifts via Concepts and Proxies.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Underspecification Presents Challenges for Credibility in Modern Machine Learning.
J. Mach. Learn. Res., 2022

Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations.
CoRR, 2022

Boosting the interpretability of clinical risk scores with intervention predictions.
CoRR, 2022

Maintaining fairness across distribution shift: do we have viable solutions for real-world applications?
CoRR, 2022

Diagnosing failures of fairness transfer across distribution shift in real-world medical settings.
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.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Causally motivated shortcut removal using auxiliary labels.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests.
CoRR, 2021

Revisiting Rashomon: A Comment on "The Two Cultures".
CoRR, 2021

SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.
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.
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.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.
CoRR, 2020

Detecting Extrapolation with Local Ensembles.
Proceedings of the 8th International Conference on Learning Representations, 2020

Fairness is not static: deeper understanding of long term fairness via simulation studies.
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.
CoRR, 2019

On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives.
CoRR, 2019

Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models.
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.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity.
CoRR, 2018

2017
Reducing Reparameterization Gradient Variance.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017


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