Overview
Background
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/.
Availability
- Dr Nan Ye is:
- Available for supervision
Research interests
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machine learning
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sequential decision making
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numerical optimization
Works
Search Professor Nan Ye’s works on UQ eSpace
2024
Journal Article
Structured neural networks for CPUE standardization: A case study of the blue endeavour prawn in Australia's Northern Prawn Fishery
Lei, Yeming, Zhou, Shijie and Ye, Nan (2024). Structured neural networks for CPUE standardization: A case study of the blue endeavour prawn in Australia's Northern Prawn Fishery. Fisheries Research, 279 107140, 107140. doi: 10.1016/j.fishres.2024.107140
2024
Journal Article
Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics
Snoswell, Aaron J., Snoswell, Centaine L. and Ye, Nan (2024). Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics. Frontiers in Digital Health, 6 1384248. doi: 10.3389/fdgth.2024.1384248
2024
Journal Article
Spatial-temporal neural networks for catch rate standardization and fish distribution modeling
Lei, Yeming, Zhou, Shijie and Ye, Nan (2024). Spatial-temporal neural networks for catch rate standardization and fish distribution modeling. Fisheries Research, 278 107097, 107097. doi: 10.1016/j.fishres.2024.107097
2024
Journal Article
Adaptive discretization using Voronoi trees for continuous pOMDPs
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2024). Adaptive discretization using Voronoi trees for continuous pOMDPs. The International Journal of Robotics Research, 43 (9), 1283-1298. doi: 10.1177/02783649231188984
2024
Conference Publication
Fast controllable diffusion models for undersampled MRI reconstruction
Jiang, Wei, Xiong, Zhuang, Liu, Feng, Ye, Nan and Sun, Hongfu (2024). Fast controllable diffusion models for undersampled MRI reconstruction. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi56570.2024.10635891
2024
Conference Publication
Robust loss functions for training decision trees with noisy labels
Wilton, Jonathan and Ye, Nan (2024). Robust loss functions for training decision trees with noisy labels. Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, BC, Canada, 20 - 28 February 2024. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i14.29516
2024
Conference Publication
A surprisingly simple continuous-action POMDP solver: lazy cross-entropy search over policy trees
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2024). A surprisingly simple continuous-action POMDP solver: lazy cross-entropy search over policy trees. 38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, Canada, 20-27 February 2024. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i18.29992
2023
Journal Article
Blockwise acceleration of alternating least squares for canonical tensor decomposition
Evans, David and Ye, Nan (2023). Blockwise acceleration of alternating least squares for canonical tensor decomposition. Numerical Linear Algebra with Applications, 30 (6) e2516. doi: 10.1002/nla.2516
2023
Journal Article
Multi-pass Bayesian estimation: a robust Bayesian method
Lei, Yeming, Zhou, Shijie, Filar, Jerzy and Ye, Nan (2023). Multi-pass Bayesian estimation: a robust Bayesian method. Computational Statistics, 39 (4), 2183-2216. doi: 10.1007/s00180-023-01390-0
2023
Journal Article
Model‐based offline reinforcement learning for sustainable fishery management
Ju, Jun, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2023). Model‐based offline reinforcement learning for sustainable fishery management. Expert Systems, 42 (1) e13324. doi: 10.1111/exsy.13324
2022
Conference Publication
Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2022). Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs. Fifteenth Workshop on the Algorithmic Foundations of Robotics WAFR 2022, College Park, MD United States, 22-24 June 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-21090-7_11
2022
Conference Publication
Positive-unlabeled learning using random forests via recursive greedy risk minimization
Wilton, Jonathan, Koay, Abigail M. Y., Ko, Ryan K. L., Miao Xu and Ye, Nan (2022). Positive-unlabeled learning using random forests via recursive greedy risk minimization. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, United States, 29 November - 1 December 2022. New Orleans, LA, United States: Neural information processing systems foundation.
2021
Conference Publication
MOOR: Model-based offline reinforcement learning for sustainable fishery management
Ju, Jun, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2021). MOOR: Model-based offline reinforcement learning for sustainable fishery management. 24th International Congress on Modelling and Simulation, Sydney, NSW, Australia, 5 - 10 December 2021. Sydney, NSW, Australia: International Congress on Modelling and Simulation. doi: 10.36334/modsim.2021.M2.ju
2021
Conference Publication
Prior versus data: A new Bayesian method for fishery stock assessment
Lei, Y., Zhou, S. and Ye, N. (2021). Prior versus data: A new Bayesian method for fishery stock assessment. 24th International Congress on Modelling and Simulation, Sydney, NSW, Australia, 5 - 10 December 2021. Sydney, NSW, Australia: International Congress on Modelling and Simulation. doi: 10.36334/modsim.2021.A1.lei
2020
Conference Publication
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms
Snoswell, Aaron J., Singh, Surya P. N. and Ye, Nan (2020). Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT Australia, 1-4 December 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/SSCI47803.2020.9308391
2020
Journal Article
Reading both single and multiple digital video clocks using context-aware pixel periodicity and deep learning
Yu, Xinguo, Song, Wu, Lyu, Xiaopan, He, Bin and Ye, Nan (2020). Reading both single and multiple digital video clocks using context-aware pixel periodicity and deep learning. International Journal of Digital Crime and Forensics, 12 (2), 21-39. doi: 10.4018/IJDCF.2020040102
2020
Conference Publication
Discriminative particle filter reinforcement learning for complex partial observations
Ma, Xiao, Karkus, Peter, Hsu, David, Lee, Wee Sun and Ye, Nan (2020). Discriminative particle filter reinforcement learning for complex partial observations. ICLR 2020: Eighth International Conference on Learning Representations, Virtual, 26 April - 1 May 2020. International Conference on Learning Representations, ICLR.
2020
Conference Publication
Greedy convex ensemble
Nguyen, Thanh Tan, Ye, Nan and Bartlett, Peter (2020). Greedy convex ensemble. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Online, 7-15 January 2021. Palo Alto, CA United States: A A A I Press. doi: 10.24963/ijcai.2020/429
2020
Journal Article
Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms
Mitchell, Drew, Ye, Nan and De Sterck, Hans (2020). Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms. Numerical Linear Algebra with Applications, 27 (4) e2297. doi: 10.1002/nla.2297
2019
Conference Publication
Maximum entropy approaches for inverse reinforcement learning
Snoswell, A. J., Singh, S. P. N. and Ye, N. (2019). Maximum entropy approaches for inverse reinforcement learning. INFORMS-APS, Brisbane, Australia, 3-5 July 2019.
Supervision
Availability
- Dr Nan Ye is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Supervision history
Current supervision
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Doctor Philosophy
Reinforcement Learning for Large and Complex Partially Observable Markov Decision Processes
Principal Advisor
Other advisors: Professor Dirk Kroese
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Doctor Philosophy
Efficient graph representation learning with neural networks and self-supervised learning
Principal Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
Data-driven framework for Sequential Decision Making in Operations Research
Principal Advisor
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Doctor Philosophy
Reinforcement Learning for Partially Observable Environments
Principal Advisor
Other advisors: Professor Dirk Kroese
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Doctor Philosophy
Machine Learning for Quantitative Fisheries Stock Assessments
Principal Advisor
Other advisors: Emeritus Professor Jerzy Filar
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Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Associate Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
High-stakes Decision Making with Weakly Supervised Data
Associate Advisor
Other advisors: Dr Miao Xu
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Doctor Philosophy
AI/ML Framework for Mixed-integer Nonlinear Optimisation
Associate Advisor
Other advisors: Professor Fred Roosta
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Doctor Philosophy
Development of novel deep learning methods for medical imaging
Associate Advisor
Other advisors: Professor Feng Liu, Dr Hongfu Sun
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Doctor Philosophy
Breast cancer metastasis prediction via machine learning and spatial cellular pathology
Associate Advisor
Other advisors: Dr Quan Nguyen
Completed supervision
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2022
Doctor Philosophy
Modelling and explaining behaviour with Inverse Reinforcement Learning: Maximum Entropy and Multiple Intent methods
Principal Advisor
Media
Enquiries
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