2 papers
stat.ML2026
ConquerNet: Convolution-Smoothed Quantile ReLU Neural Networks with Minimax Guarantees
Tianpai Luo, Fangwei Wu, Weichi Wu
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. W…
cs.CR2025
Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
Sanghyun Hong, Fan Wu, Anthony Gruber +1
In this work, we study the feasibility of using neural ordinary differential equations (NODEs) to model systems with intrinsic privacy properties. Unlike conventional feedforward n…