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Award ID contains: 2232054

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  1. ; ; ; ( , arXivorg)
    Accepted Manuscript Full Text Available
  2. ; ; ( , International Joint Conference on Artificial Intelligence)

    Model selection is a strategy aimed at creating accurate and robust models by identifying the optimal model for classifying any particular input sample. This paper proposes a novel framework for differentiable selection of groups of models by integrating machine learning and combinatorial optimization.The framework is tailored for ensemble learning with a strategy that learns to combine the predictions of appropriately selected pre-trained ensemble models. It does so by modeling the ensemble learning task as a differentiable selection program trained end-to-end over a pretrained ensemble to optimize task performance. The proposed framework demonstrates its versatility and effectiveness, outperforming conventional and advanced consensus rules across a variety of classification tasks.

     
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  3. ; ; ( , arXivorg)
    Accepted Manuscript Full Text Available