A graduated optimization strategy based on a surrogate loss that decouples the variance term from the reconstruction loss, thereby preventing early gradient attenuation and guides the learned DAG to better reflect the data.
Eunjung Choi, Seonggyeom Kim, Dong-Kyu Chae· Proceedings of the 32nd ACM...· 0 citations
Causal Structure-guided DRO (CS-DRO) is proposed, which estimates a directed acyclic graph (DAG) that encodes the predictive relationships between representations and labels, serving as a proxy for causal structure shared across source domains.
Seonggyeom Kim, Eunjung Choi, Dong-Kyu Chae· Proceedings of the 32nd ACM...· 0 citations
Domain generalization (DG) aims to learn predictive models from multiple source domains that maintain performance on unseen target domains. Distributionally robust optimization (DRO) addresses distribution shift by minimizing the worst-case risk over an uncertainty set of plausible test distributions. However, if this...
Seonggyeom Kim, Eunjung Choi, Dong-Kyu Chae· Proceedings of the 32nd ACM...· 0 citations
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