Misclassification excess risk bounds for PAC-Bayesian classification via convexified loss
<|reference_start|>Misclassification excess risk bounds for PAC-Bayesian classification via convexified loss: PAC-Bayesian bounds have proven to be a valuable tool for deriving generalization bounds and for designing new learning algorithms in machine learning. However, it typically focus on providing generalization bounds with respect to a chosen loss function. In classification tasks, due to the non-convex nature of the 0-1 loss, a convex surrogate loss is often used, and thus current PAC-Bayesian bounds are primarily specified for this convex surrogate. This work shifts its focus to providing misclassification excess risk bounds for PAC-Bayesian classification when using a convex surrogate loss. Our key ingredient here is to leverage PAC-Bayesian relative bounds in expectation rather than relying on PAC-Bayesian bounds in probability. We demonstrate our approach in several important applications.<|reference_end|>
@article{mai2024misclassification,
    title={Misclassification excess risk bounds for PAC-Bayesian classification via
  convexified loss},
    author={The Tien Mai},
    journal={arXiv preprint arXiv:2408.08675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08675},
    primaryClass={stat.ML cs.LG}
}