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News/ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees
arxiv
PublishedMay 8, 2026 at 4:00 AM
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ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees

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arXiv:2605.06265v1 Announce Type: cross Abstract: 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

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