AI RESEARCH
ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees
arXiv CS.LG
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ArXi:2605.06265v1 Announce Type: cross Quantile regression is a fundamental tool for distributional learning but poses significant optimization challenges for deep models due to the non-smoothness of the pinball loss. We propose ConquerNet, a class of \textbf{con}volution-smoothed \textbf{qu}antil\textbf{e} \textbf{R}eLU neural \textbf{net}works, which yield smooth objectives while preserving the underlying quantile structure. We establish general nonasymptotic risk bounds for ConquerNet under mild conditions, providing minimax guarantees over Beso function classes.