- Speaker 1: Xinwei Shen (ETH Zurich)
- Title: Causality-oriented robustness: exploiting data heterogeneity at different levels
- Abstract: Since distribution shifts are common in real-world applications, there is a pressing need for developing prediction models that are robust against such shifts. Unlike empirical risk minimization or distributionally robust optimization, causality offers a data-driven and structural perspective to robust prediction. In this talk, we discuss causality-oriented robust prediction by exploiting heterogeneity in multi-environment training data at different levels. Previous work such as anchor regression has mainly studied mean shifts, while we propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits variance shifts induced by general additive interventions for robust prediction against more diverse unseen interventions. Finally, we discuss an idea to go beyond specific characteristics but exploit shifts in overall aspects of the distribution, thus leading to potentially more robust predictions. The proposed methods are validated on a single-cell data application.
Негізгі бет Xinwei Shen: Causality-oriented robustness: exploiting data heterogeneity at different levels
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