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Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data.
Towards Stability of Parameter-free Optimization
To tackle this challenge, in this paper, we propose a novel parameter-free optimizer, \textsc{AdamG} (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning.
Cross-modality debiasing: using language to mitigate sub-population shifts in imaging
Specifically, in the context of the distributional robustness of CLIP, we propose to leverage natural language inputs to debias the image feature representations, to improve worst-case performance on sub-populations.
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