Deep Distributional Learning with Non-crossing Quantile Network

• URL: http://arxiv.org/abs/2504.08215v1
• Date: Fri, 11 Apr 2025 02:46:39 GMT
• Title: Deep Distributional Learning with Non-crossing Quantile Network
• Authors: Guohao Shen, Runpeng Dai, Guojun Wu, Shikai Luo, Chengchun Shi, Hongtu Zhu,
• Abstract summary: We introduce a non-crossing quantile (NQ) network for conditional distribution learning.<n>By leveraging non-negative activation functions, the NQ network ensures that the learned distributions remain monotonic.<n>The NQ network-based deep distributional learning framework is highly adaptable.
• Score: 17.3807089737521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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