Nan Ye

Nan Ye's research interest spans machine learning, statistics and optimization. He has published papers on topics including sequential decision making under uncertainty, weakly supervised learning, probabilistic graphical models, statistical learning theory, in venues such as NeurIPS, ICML, ICLR, UAI, JAIR, JMLR. He received an IJCAI-JAIR Best Paper Prize in 2022, and a UAI Best Student Paper Award in 2014. He is a Lecturer in Statistics and Data Science in the School of Mathematics and Physics in University of Queensland. He previously held postdoc positions at QUT and UC Berkeley from 2015 to 2018, and at NUS from 2013 to 2014. He obtained his PhD in Computer Science from NUS, and completed double first-class honors in Computer Science and Applied Mathematics, also from NUS. Please visit his personal webpage for more information: https://yenan.github.io/. machine learning, sequential decision making, numerical optimization

[2026] Optimal Policy Analysis for Monotonic (PO)MDPs and an Application to Fishery Management [2026] Hierarchical cluster-based open-world graph active learning [2026] Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data [2026] Generalized Few-Shot Node Classification via Training Set Refinement [2025] Decision-making under uncertainty: a multidisciplinary perspective [2025] Physics-informed surrogate for cardiovascular flow extrapolation through transductive learning [2025] Spanish-language text classification for environmental evidence synthesis using multilingual pre-trained models [2025] Inductive graph few-shot class incremental learning [2024] A boosting framework for positive-unlabeled learning [2024] Structured neural networks for CP