Boosting is a powerful method that turns weak learners, which perform only slightly better than random guessing, into strong learners with high accuracy. While boosting is well understood in the classic setting, it is less so in the agnostic case, where no assumptions are made about the data. Indeed, only recently was the sample complexity of agnostic boosting nearly settled arXiv:2503.09384, but the known algorithm achieving this bound has exponential running time. In this work, we propose the first agnostic boosting algorithm with near-optimal sample complexity, running in time polynomial in the sample size when considering the other parameters of the problem fixed.

翻译：提升是一种强大的方法，它能够将仅比随机猜测略好的弱学习器转化为具有高准确性的强学习器。虽然在经典设定下提升方法已得到充分理解，但在不可知情形下则不然，后者不对数据做任何假设。事实上，直到最近arXiv:2503.09384才几乎确定了不可知提升的样本复杂度，但已知达到该界限的算法具有指数级的运行时间。在本工作中，我们提出了第一个具有近最优样本复杂度的不可知提升算法，当考虑问题的其他参数固定时，其运行时间在样本大小上是多项式级的。