430 citations · 856 across the 28 of their papers we have counts for
30 papers
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
When does Privileged Information Explain Away Label Noise?
Guillermo Ortiz-Jimenez, Mark Collier, Anant Nawalgaria +4
Leveraging privileged information (PI), or features available during training but not at test time, has recently been shown to be an effective method for addressing label noise. Ho…
Adapting to Latent Subgroup Shifts via Concepts and Proxies
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D'Amour +7
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When thi…
Beyond Invariance: Test-Time Label-Shift Adaptation for Distributions with "Spurious" Correlations
Qingyao Sun, Kevin Murphy, Sayna Ebrahimi +1
Changes in the data distribution at test time can have deleterious effects on the performance of predictive models . We consider situations where there are additional meta-…
Fairness and robustness in anti-causal prediction
Maggie Makar, Alexander D'Amour
Robustness to distribution shift and fairness have independently emerged as two important desiderata required of modern machine learning models. While these two desiderata seem rel…
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
Jiajing Zheng, Jiaxi Wu, Alexander D'Amour +1
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-o…