【报告摘要】In this talk, 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.

 【报告人简介】朱宏图博士是北卡罗来纳大学教堂山分校生物统计学、统计学、放射学、计算机科学及遗传学系的 Kenan 杰出教授，曾任滴滴出行首席科学家及 MD 安德森癌症中心讲席教授。作为统计学习、医学影像分析及人工智能领域的国际顶尖专家，他在《Nature》、《Science》、《Cell》及统计学四大顶刊发表论文 345 余篇，并在 NeurIPS 等 AI 顶会发表 58 篇以上论文，曾荣获 COPSS Snedecor 奖（2025）和 IMS Medallion 奖（2027）等重要荣誉，目前还担任统计学顶级期刊 JASA 的协调主编和ACS的主编。