Dr Nan Ye
Lecturer in Statistics&Data Science
School of Mathematics and Physics
+61 7 334 69095
| Featured projects | Duration |
|---|---|
|
Real-time Automatic Detection of Cyber-Attacks using Weakly Supervised Learning UQ Cyber Seed Funding |
2021–2022 |
|
“Are Power Transformers Hackable?” - Leveraging Security and Reliability of Large Power Transformers in Australia’s Power Grids UQ Cyber Seed Funding |
2023 |
Book Chapter
Ye, Nan, Roosta-Khorasani, Farbod and Cui, Tiangang (2019). Optimization methods for inverse problems. 2017 MATRIX annals. (pp. 121-140) edited by David R. Wood, Jan de Gier, Cheryl E. Praeger and Terence Tao. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04161-8_9
Journal Articles
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
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
Avrachenkov, Konstantin, Prałat, Paweł and Ye, Nan (2019). Preface. Lecture Notes in Computer Science, 11631 LNCS.
Yu, Xinguo, Wang, Mingshu, Gan, Wenbin, He, Bin and Ye, Nan (2018). A framework for solving explicit arithmetic word problems and proving plane geometry theorems. International Journal of Pattern Recognition and Artificial Intelligence, 33 (7) 1940005, 1940005. doi: 10.1142/S0218001419400056
Conference Papers
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
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:
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
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
Filar, Jerzy A., Qiao, Zhihao and Ye, Nan (2019). POMDPs for sustainable fishery management. International Congress on Modelling and Simulation, Canberra, Australia, 1-6 December 2019. Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2019.g2.filar
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.
Bai, Haoyu, Cai, Shaojun, Ye, Nan, Hsu, David and Lee, Wee Sun (2015). Intention-aware online POMDP planning for autonomous driving in a crowd. 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA United States, 26-30 May 2015. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICRA.2015.7139219
Ye, Nan, Chai, Kian Ming A., Lee, Wee Sun and Chieu, Hai Leong (2012). Optimizing F-measures: A tale of two approaches. 29th International Conference on Machine Learning, ICML 2012, Edinburgh, United Kingdom, 26 June - 1 July 2012. New York, NY United States: Association for Computing Machinery.
Ye, Nan, Lee, Wee Sun, Chieu, Hai Leong and Wu, Dan (2009). Conditional random fields with high-order features for sequence labeling. 23rd Annual Conference on Neural Information Processing Systems 2009, Vancouver, Canada, 7-10 December 2009. Curran Associates.
Jain, Sanjay, Stephan, Frank and Ye, Nan (2009). Learning from streams. 20th International Conference of Algorithmic Learning Theory ALT 2009, Porto, Portugal, 3-5 October 2009. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-04414-4_28
Wu, Dan, Lee, Wee Sun, Ye, Nan and Chieu, Hai Leong (2009). Domain adaptive bootstrapping for named entity recognition. 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 6 - 7 August 2009. Stroudsburg, PA United States: Association for Computational Linguistics. doi: 10.3115/1699648.1699699
Ning, Kang, Ye, Nan and Leong, Hon Wai (2008). On preprocessing and antisymmetry in de novo peptide sequencing: Improving efficiency and accuracy. Computational Systems Bioinformatics 2007, San Diego, CA United States, 13-17 August 2007. London, United Kingdom: World Scientific Publishing. doi: 10.1142/S0219720008003503