The 21st Australasian Data Science and Machine Learning Conference (AUSDM'23)

Auckland, New Zealand, 11-13 December 2023

Accepted Papers

Speaker instructions (research and application tracks):
The AusDM research and application tracks program consists of oral presentations.
Each paper is allocated 15 minutes of talk time + 5 minutes of Q&A. It is important to present a high-level perspective of your contribution. 
The presentations schedule is available at: Please check your assigned session and be mindful of day and time. The session chair will collect presentations before the start of the session. Therefore, it is required that you arrive to your session at least 10 minutes before the start of the session. Please bring your presentation slides in a USB.  Rooms for each session will be posted on the website close to the conference dates.

Research Track

Nan Yang, Laicheng Zhong, Fan Huang, Wei Bao and Dong Yuan. Random Padding Data Augmentation

Shaowen Tang and Raymond Wong Unsupervised Fraud Detection on Sparse Rating Networks

Ying Cui, Louise McMillan and Ivy Liu Semi-supervised model-based clustering for ordinal data

Ali Anaissi, Yuanzhe Jia, Ali Braytee, Mohamad Naji and Widad Alyassine Damage GAN: A Generative Model for Imbalanced Data

Michael Longland, David Liebowitz, Kristen Moore and Salil Kanhere Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders

Liang Tang, Qianqian Qi, Qinghua Shang, Yuguang Cai, Jiamou Liu, Michael Witbrock and Kaokao Lv Boosting QA Performance through SA-Net and AA-Net with the Read+Verify Framework

Sadeq Darrab, Harshitha Allipilli, Sana Ghani, Harikrishnan Changaramkulath, Sricharan Koneru, David Broneske and Gunter Saake Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives

Manh Khoi Duong and Stefan Conrad Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes

Kamaladdin Fataliyev and Wei Liu MStoCast: Multimodal Deep Network for Stock Market Forecast

Sayed Waleed Qayyumi, Laurence Park and Oliver Obst Few shot and transfer learning with manifold distributed datasets

Din Sangrasi, Lei Wang, Markus Hagenbuchner and Peng Wang Mitigating The Adverse Effects of Long-tailed Data on Deep Learning Models

Chunyu Wang, Qi Chen, Bing Xue and Mengjie Zhang Shapley Value Based Feature Selection to Improve Generalization of Genetic Programming for High-Dimensional Symbolic Regression

Tulika Shrivastava, Basem Suleiman and Muhammad Johan Alibasa Hybrid Models for Predicting Cryptocurrency Price using Financial and Non-Financial Indicators

Applications Track

Amit Vurgaft Multi-Dimensional Data Visualization for Analyzing Materials

Tobias Milz, Elizabeth Macpherson and Varvara Vetrova Law in Order: An Open Legal Citation Network for New Zealand

Li Xiao, Samaneh Madanian, Weihua Li and Yuchun Xiao Enhancing Resource Allocation in IT Projects: The Potentials of Deep Learning-Based Recommendation Systems and Data-Driven Approaches

Fathima Nuzla Ismail, Abira Sengupta, Brendon J. Woodford and Sherlock Licorish A Comparison of One-class versus Two-class Machine Learning Models for Wildfire Prediction in California

Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue and Mengjie Zhang Skin Cancer Detection with Multimodal Data: A Feature Selection Approach Using Genetic Programming

Prathayne Nanthakumaran and Liwan Liyanage Comparison of Interpolation techniques for Prolonged Exposure Estimation: A Case Study on Seven years of Daily Nitrogen Oxide in Greater Sydney

Sedigh Khademi, Christopher Palmer, Muhammad Javed, Gerardo Luis Dimaguila, Jim Buttery and Jim Black Detecting asthma presentations from emergency department notes: An active learning approach

Days until Conference








Keydates (AoE)

Abstract submission: 18 Aug 23
Paper submission: 25 Aug 23
Paper notification: 24 Sept 23
Tutorial/Workshop submission: 26 Sept 23
Tutorial/Workshop notification: 29 Sept 23
Camera-ready: 8 Oct 23
Author Registration: 8 Oct 23

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