Nan Ye's Homepage

I am a Lecturer in Statistics and Data Science in the School of Mathematics and Physics in University of Queensland. I previously held postdoc positions at QUT and UC Berkeley from 2015 to 2018, and at NUS from 2013 to 2014. Before that, I earned my PhD in Computer Science from NUS, and completed double first-class honors in Computer Science and Applied Mathematics, also from NUS.

I am broadly interested in machine learning algorithms, theory, and applications. I have 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. I have received an IJCAI-JAIR Best Paper Prize in 2022, and a UAI Best Student Paper Award in 2014.

I am seeking highly motivated students to work with me on machine learning research. If you are interested, please email me your CV/transcripts, a sample of your writing, and a brief description of your research interests.

News

2023

[07/09] Our paper on a Bayesian method that is robust against prior misspecification is accepted at Computational Statistics. [PDF]
[06/20] Our paper on a continuous POMDP solver is accepted by IJRR. [PDF]
[06/06] I am serving AAAI'23 as an SPC member.
[06/02] Our paper on an accelerated ALS algorithm is accepted by NLA. [PDF]
[04/17] I am serving NeurIPS'23 as a reviewer.
[04/17] Our paper on a model-based offline reinforcement learning approach for sustainable fishery management is accepted by Expert Systems. [PDF]
[02/08] I gave a tutorial on machine learning for applied mathematicians in ANZIAM'23. [slides]

2022

[11/29] I will give a tutorial on machine learning for applied mathematicians in ANZIAM'23.
[11/28] I am serving IJCAI'23 as an SPC member.
[09/15] Our paper on random forests for positive-unlabeled learning is accepted by NeurIPS. [PDF]
[07/28] Our paper DESPOT: Online POMDP Planning with Regularization received the 2022 IJCAI-JAIR Best Paper Prize.
[08/19] We are organising a Special Issue of the Annals of Operations Research on Decision Making Under Uncertainty and calling for papers.
[07/20] I am serving as an organising committee member for ANZIAM'23.
[07/19] I am serving AAAI'23 as an SPC member.
[05/12] We are organizing a Workshop on Decision Making under Uncertainty and calling for participation.
[22/04] I am selected as a Highlighted Reviewer for ICLR'22.
[04/01] Our paper on solving continuous-action POMDPs is accepted at WAFR'23. [PDF]

Group members

Current members

Marcus Hoerger (Postdoc)
Jun Ju (PhD)
Yeming Lei (PhD)
Jonathan Wilton (PhD)

Past members

Aaron Snoswell (PhD, 2022)
Tan Nguyen (PhD, 2020)
Piyush Patil (Masters, 2021)
Cameron Gordon (Masters, 2020)
Yuting Yang (Masters, 2020)
Yutong Chen (Masters, 2020)
Robert Wall (Masters, 2019)
David Evans (Masters, 2019)
Jacky Xie (Honours, 2022)
Montana Wickens (Honours, 2022)
David Maine (Honours, 2021)
Daniel Noar (Honours, 2020)
Scott Whittington (Honours, 2020)
Naziah Saddique (Honours, 2020)
Drew Mitchell (Honours, 2018)

Software

DESPOT - Online solver for large POMDPs.
MaxEntIRL - Maximum Entropy IRL.
PUExtraTrees - Extra Trees for PU-learning.
HOSemiCRF - High-order Semi-Markov Conditional Random Field.

Publications

Please see my Google Scholar profile for the full list of my publications.

Positive-unlabeled learning using random forests via recursive greedy risk minimization.
Jonathan Wilton, Abigail MY Koay, Ryan KL Ko, Miao Xu, and Nan Ye.
In Advances In Neural Information Processing Systems, 2022
Adaptive Discretization using Voronoi Trees for Continuous-Action POMDPs.
Marcus Hoerger, Hanna Kurniawati, Dirk Kroese, and Nan Ye.
In WAFR, 2022
Prior versus data: A new Bayesian method for fishery stock assessment.
Yeming Lei, Shijie Zhou, and Nan Ye.
In MODSIM, 2021
MOOR: Model-based Offline Reinforcement Learning for Sustainable Fishery Management.
Jun Ju, Hanna Kurniawati, Dirk Kroese, and Nan Ye.
In MODSIM, 2021
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms.
Aaron J. Snoswell, Surya P. N. Singh, and Nan Ye.
In IEEE Symposium Series on Compu- tational Intelligence, 2020.
Greedy Convex Ensemble.
Thanh Tan Nguyen, Nan Ye, and Peter Bartlett.
In IJCAI, 2020.
Discriminative particle filter reinforcement learning for complex partial observations.
Xiao Ma, Peter Karkus, David Hsu, Wee Sun Lee, and Nan Ye.
In ICLR, 2020.
Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms.
Drew Mitchell, Nan Ye, and Hans De Sterck.
Numerical Linear Algebra with Applications, 2020.
Reading Both Single and Multiple Digital Video Clocks Using Context-Aware Pixel Periodicity and Deep Learning.
Xinguo Yu, Wu Song, Xiaopan Lyu, Bin He, and Nan Ye.
International Journal of Digital Crime and Forensics (IJDCF), 12(2):21–39, 2020.
POMDPs for sustainable fishery management.
Jerzy A Filar, Zhihao Qiao, and Nan Ye.
In International Congress on Modelling and Simulation, 2019.
A Framework for Solving Explicit Arithmetic Word Problems and Proving Plane Geometry Theorems.
Xinguo Yu, Mingshu Wang, Wenbin Gan, Bin He, and Nan Ye.
International Journal of Pattern Recognition and Artificial Intelligence, page 1940005, 2018.
Tensor Belief Propagation.
Andrew Wrigley, Wee Sun Lee, and Nan Ye.
In Proceedings of the International Conference on Machine Learning, 2017.
Modelling imperfect presence data obtained by citizen science.
Kerrie Mengersen, Erin E. Peterson, Sam Clifford, Nan Ye, June Kim, Tomasz Bednarz, Ross Brown, Allan James, Julie Vercelloni, Alan R. Pearse, Jacqueline Davis, and Vanessa Hunter.
Environmetrics, 2017.
DESPOT: Online POMDP Planning with Regularization.
Nan Ye, Adhiraj Somani, David Hsu, and Wee Sun Lee.
Journal of Artificial Intelligence Research, 58:231–266, 2017.
Optimization Methods for Inverse Problems.
Nan Ye, Farbod Roosta-Khorasani, and Tiangang Cui.
In David R. Wood, Jan de Gier, Cheryl E. Praeger, and Terence Tao, editors, MATRIX Annals, volume 2. Springer, 2017
Robustness of Bayesian Pool-based Active Learning Against Prior Misspecification. [PDF]
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee.
AAAI 2016.
Intention-Aware Online POMDP Planning for Autonomous Driving in a Crowd. [PDF]
Haoyu Bai, Shaojun Cai, Nan Ye, David Hsu, Wee Sun Lee.
ICRA 2015.
Near-optimal Adaptive Pool-based Active Learning with General Loss. [PDF]
Google Best Student Paper Award
Nguyen Viet Cuong, Wee Sun Lee, Nan Ye.
UAI 2014.
Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation. [PDF] [Code]
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu.
JMLR 15 (Mar):981−1009, 2014.
DESPOT: Online POMDP Planning with Regularization. [PDF] [Supplement] [Code]
Adhiraj Somani, Nan Ye, David Hsu, Wee Sun Lee.
NIPS 2013.
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion. [PDF]
Nguyen Viet Cuong, Wee Sun Lee, Nan Ye, Kian Ming A. Chai, and Hai Leong Chieu.
NIPS 2013.
Optimizing F-measures: A Tale of Two Approaches. [PDF] [Supplement] [Code]
Nan Ye, Kian Ming A. Chai, Wee Sun Lee, and Hai Leong Chieu.
ICML 2012.
Semi-Markov Conditional Random Field with High-Order Features. [PDF] [Code]
Viet Cuong Nguyen, Nan Ye, Wee Sun Lee, and Hai Leong Chieu.
ICML Workshop on Structured Sparsity: Learning and Inference, 2011.
Learning from Streams. [PDF]
Sanjay Jain, Frank Stephan, and Nan Ye.
ALT 2009: 338-352.
Domain adaptive bootstrapping for named entity recognition. [PDF]
Dan Wu, Wee Sun Lee, Nan Ye and Hai Leong Chieu.
EMNLP 2009: 1523-1532.
Conditional Random Fields with High-Order Features for Sequence Labeling. [PDF]
Nan Ye, Wee Sun Lee, Hai Leong Chieu and Dan Wu.
NIPS 2009: 2196-2204.
Prescribed Learning of R.E. Classes. [PDF]
Sanjay Jain, Frank Stephan and Nan Ye.
Theoretical Compututer Science 410(19): 1796-1806 (2009).
Prescribed Learning of Indexed Families. [PDF]
Sanjay Jain, Frank Stephan and Nan Ye.
Fundamental Informatica 83(1-2): 159-175 (2008).
On Preprocessing and Antisymmetry in de novo Peptide Sequencing: Improving Efficiency and Accuracy.
Kang Ning, Nan Ye and Hon Wai Leong.
Journal of Bioinformatics and Computational Biology 6(3): 467-492 (2008).
Prescribed Learning of R.E. Classes. [PDF]
Sanjay Jain, Frank Stephan and Nan Ye.
ALT 2007: 64-78.

Teaching

2024 1-5 Jul. AMSI Winter School. Reinforcement Learning
2022 Semester 2. DATA7703 Machine Learning for Data Scientists.
2022 Semester 1. STAT3007/7007 Deep Learning.
2022 Semester 1. STAT2201 Analysis of Engineering & Scientific Data.
2021 Semester 2. STAT2201 Analysis of Engineering & Scientific Data.
2021 Semester 2. DATA7703 Machine Learning for Data Scientists.
2021 Semester 1. STAT3007/7007 Deep Learning.
2020 Semester 2. DATA7703 Machine Learning for Data Scientists.
2020 Semester 2. STAT4402/7503 Advanced Statistics II (Deep Learning).
2020 Semester 1. DATA7001 Introduction to Data Science.
2019 Semester 2. STAT4402/7503 Advanced Statistics II (Deep Learning).
2019 Semester 2. STAT3500/7500 Problems & Applications in Modern Statistics.
2019 Semester 2. DATA7001 Introduction to Data Science.
2019 Semester 1. DATA7001 Introduction to Data Science.
2018 Semester 2. STAT3500/7500 Problems & Applications in Modern Statistics.
2015 Semester 1. MXB361 Aspects of Computational Science.