两篇新研究论文探讨了Reproducing kernel Hilbert spaces中的高级非参数回归技术。第一篇论文详细介绍了正则化M估计的综合理论，为各种损失函数建立了存在性和可测量性，并证明了尖锐的收敛速度。第二篇论文介绍了一种在这些空间中进行监督学习的子采样方案，旨在降低计算成本同时保持准确性，并通过数值研究证明了其可行性。 AI
影响 这些论文推进了机器学习的理论理解，可能导致更有效、更准确的复杂数据分析算法。
排序理由 两篇在arXiv上发表的学术论文，详细介绍了机器学习的理论进展。
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We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses…
We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses…
In the era of big data, subsampling became a common practice in statistical learning. By selecting a subgroup of individuals based on which the learner is trained, subsampling aims at reducing the computational cost and time of the estimation step, and ideally leads to a decrease…