Deep Distributional Learning with Non-crossing Quantile Network
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910908995862528 |
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| author | Shen, Guohao Dai, Runpeng Wu, Guojun Luo, Shikai Shi, Chengchun Zhu, Hongtu |
| author_facet | Shen, Guohao Dai, Runpeng Wu, Guojun Luo, Shikai Shi, Chengchun Zhu, Hongtu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08215 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Distributional Learning with Non-crossing Quantile Network Shen, Guohao Dai, Runpeng Wu, Guojun Luo, Shikai Shi, Chengchun Zhu, Hongtu Machine Learning Statistics Theory 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. |
| title | Deep Distributional Learning with Non-crossing Quantile Network |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2504.08215 |