Machine learning
and Decision Making:
Theory, Algorithms and Applications

About this symposium
Machine learning and decision making, considered essential parts in artificial intelligence, play an increasingly important role in providing solutions to complex problems in a broad range of domains.This includes but is not limited to image recognition, self-driving cars, virtual personal assistants,games and adaptive natural resource management.
Recently, the field has made significant advances through an interplay of theory, algorithms and applications. For example, mathematical and statistical theories play a multi-faceted role: powering the design, analysis and improvement of algorithms; providing insights on when and why algorithms work; constructing guidelines on how to effectively apply algorithms in applications from different disciplines. In addition, the emergence of challenging applications stimulates the development of more powerful algorithms and new theories.
This symposium calls for the participation of PhD students working on the theory, algorithms and applications in the field of machine learning and decision making. It aims to bring together students from all related disciplines to present their research to a wide audience, as well as to connect, interact, and exchange ideas with other students. The symposium will include a series of invited talks, tutorials,student contributed talks, and a number of events to encourage discussion and collaboration between students.

Invited talks


Lecturer (Statistics & Data Science)
School of Mathematics and Physics
University of Queensland


University of Queensland


Associate Professor
Australian National University


Associate Professor
University of Queensland

Duy Nguyen

Senior Lecturer
University of Queensland


Research Fellow
Australian National University


The symposium will be held online
Time Zone - AEST (Brisbane time)

Day 1 (Thursday, 17th March 2022)
9:30am - 9:40am Welcome
9:40am - 10:20am Dirk Kroese
Simulating Random Variables via Neural Networks
10:20am - 10:40am Break
10:40am - 11:20am Marcus Gallagher
Analysing optimization problems from clustering
11:20am - 12:30pm Lunch
12:30pm - 2:30pm Contributed Talks
2:30pm - 2:50pm Break
2:50pm - 3:50pm Nan Ye
Reinforcement learning: A bird's eye view
Day 2 (Friday, 18th March 2022)
9:30am - 9:40am Welcome
9:40am - 10:20am Hanna Kurniawati
Practical Partially Observable Markov Decision Processes: A Step Towards General Purpose Robots
10:20am - 10:40am Break
10:40am - 11:20am Hien Duy Nguyen
The MM algorithm in machine learning
11:20am - 11:40pm Break
11:40am - 12:20pm Marcus Hoerger
Tractable Online POMDP Planning: Challenges and Methods


Registration has closed.

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