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Results show that the effectiveness of TabPFN is highly problem dependent, and it cannot replace conventional surrogates universally, and it should be adopted selectively according to data availability, landscape complexity, search space characteristics, and its role within the algorithm.
CRIP selectively fuses features from clients based on representational similarity, leading to superior performance in one-shot federated learning under extreme domain heterogeneity.
Forget hand-tuning: AutoSG automatically conjures high-performing solvers for expensive optimization problems directly from task prompts.
