Deep Distributional Learning with Non-crossing Quantile Network

Please join the GW Department of Statistics for a seminar with Hongtu Zhu, PhD, Kenan Distinguished Professor at the University of North Carolina at Chapel Hill.

Abstract:

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.

About the speaker:

Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, the ICSA 2025 Distinguished Achievement Award, the IMS 2027 Medallion award and Lecture and the COPSS 2025 Snedecor Award. He has published more than 350 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, Journal of the American Statistical Association (JASA), Biometrika and JRSSB, as well as presenting 61+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.