提出非交叉分位数网络，解决分布学习中分位数交叉问题。
Deep Distributional Learning with Non-crossing Quantile Network
- 使用非负激活函数保证分位数单调性，避免交叉
- 在因果效应估计和分布强化学习中表现稳健
- 理论完备，适合需要可靠分布建模的研究者
本文提出一种非交叉分位数（NQ）网络用于条件分布学习。通过采用非负激活函数，NQ网络确保学习到的分布具有单调性，有效解决了分位数交叉问题。基于NQ网络的深度分布学习框架具有高度可扩展性，适用于从经典非参数分位数回归到因果效应估计、分布强化学习（RL）等复杂任务。我们还为深度NQ估计器及其在分布强化学习中的应用建立了完整的理论基础，深入分析证明了其在多个领域中的有效性。实验结果进一步验证了NQ网络的鲁棒性与通用性。
原文摘要 · Abstract (English)
In this paper, we introduce a non-crossing quantile (NQ) network for conditional distribution learning. By leveraging non-negative activation functions, the NQ network ensures that the learned distributions remain monotonic, effectively addressing the issue of quantile crossing. Furthermore, the NQ network-based deep distributional learning framework is highly adaptable, applicable to a wide range of applications, from classical non-parametric quantile regression to more advanced tasks such as causal effect estimation and distributional reinforcement learning (RL). We also develop a comprehensive theoretical foundation for the deep NQ estimator and its application to distributional RL, providing an in-depth analysis that demonstrates its effectiveness across these domains. Our experimental results further highlight the robustness and versatility of the NQ network.
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