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University of Minnesota
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Parameter-efficient adaptation methods can match full fine-tuning gains for tabular foundation models, while selective aggregation offers a clear path to improved predictions over ensemble averaging.
Signal processing offers a surprisingly effective lens for understanding and improving LoRA, the reigning champ of parameter-efficient fine-tuning.
