content type paperpublished July 2026
Anti-Causal Domain Generalization: Leveraging Unlabeled Data
AuthorsSorawit Saengkyongam†, Juan L. Gamella, Andrew C. Miller†, Jonas Peters‡, Nicolai Meinshausen‡, Christina Heinze-Deml†
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The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require labeled data from multiple training environments, limiting their applicability when labeled data are scarce. In this work, we study domain generalization in an anti-causal setting, where the outcome causes the observed covariates. Under this structure, environment perturbations that affect the covariates do not propagate to the outcome, which motivates regularizing the model’s sensitivity to these perturbations. Crucially, estimating these perturbation directions does not require labels, enabling us to leverage unlabeled data from multiple environments. We propose two methods that penalize the model’s sensitivity to variations in the mean and covariance of the covariates across environments, respectively, and prove that these methods have worst-case optimality guarantees under certain classes of environments. Finally, we demonstrate the empirical performance of our approach on a controlled physical system and a physiological signal dataset.
† Apple
‡ ETH Zürich
Considerations for Distribution Shift Robustness in Health
May 2, 2023research area Health, research area Methods and Algorithmsconference ICLR
*=Equal Contributors
This paper was accepted at the workshop “Trustworthy Machine Learning for Healthcare Workshop” at the conference ICLR 2023.
When analyzing robustness of predictive models under distribution shift, many works focus on tackling generalization in the presence of spurious correlations. In this case, one typically makes use of covariates or environment indicators to enforce independencies in learned models to guarantee…
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More Speaking or More Speakers?
March 14, 2023research area Speech and Natural Language Processingconference ICASSP
Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best of our knowledge, there is no analysis of how the composition of the labelled and unlabelled datasets used in these methods affects the results. In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0),…
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