Variational Inference for Scalable 3D Object-centric Learning

October 1, 2023

My phd student has just released a paper on 3D Object-Centric Learning on arXiv. I am pretty proud of the work, although I really only understand around 40% of it. Here’s the abstract: We tackle the task of scalable unsupervised object-centric representation learning on 3D scenes. Existing approaches to object-centric representation learning show limitations in … More Variational Inference for Scalable 3D Object-centric Learning

A Simple Definition of Artificial Intelligence

August 6, 2023

There are many different definitions of Artificial Intelligence in the literature, all are suggestive and insightful. However, at the end of the day, I think there is really one simple enough to be understood and formalised rigorously. This is John McCarthy’s original definition of AI from 1955: “the science and engineering of making intelligent machines”. … More A Simple Definition of Artificial Intelligence

FinTracer and Friends

June 25, 2023

About 5 years ago, Tania Churchill and I assembled a team of researchers and engineers across AUSTRAC and ANU to work on privacy technologies for detecting criminal activities across the financial system, funded by the Fintel Alliance Expansion budget measure, the Investigative Analytics NPP (led by CSIRO’s Data61), and an ANU Translational Fellowship. The overall … More FinTracer and Friends

Split Count and Share: A Differentially Private Set Intersection Cardinality Algorithm

June 11, 2023

My colleagues Mike Purcell, Kelvin Yang Li and I have a new paper on differentially private set intersection cardinality algorithm accepted at this year’s Uncertainty in Artificial Intelligence conference. Here is the abstract:We describe a simple two-party protocol in which each party contributes a set as input. The output of the protocol is an estimate … More Split Count and Share: A Differentially Private Set Intersection Cardinality Algorithm

A Map of Machine Learning Principles and Algorithms

April 25, 2023

Here is my attempt to map out the major classes of algorithms in Machine Learning, organised around the associated induction principles and learning theory. The usual caveats apply around this being biased towards my own experience. At the highest level, we can distinguish between the Passive and Active learning settings. In the passive case, the … More A Map of Machine Learning Principles and Algorithms

A Direct Approximation of AIXI using Logical State Abstractions

October 15, 2022

Artificial Intelligence as a well-defined mathematical problem was solved a number of years ago through the formulation of the AIXI agent by Prof Marcus Hutter — see https://theconversation.com/to-create-a-super-intelligent-machine-start-with-an-equation-20756 for a quick introduction — but a key fundamental issue with the AIXI theory has always been the incomputability of the general solution. In a continuation of … More A Direct Approximation of AIXI using Logical State Abstractions

A Tutorial Introduction to Lattice-based Cryptography and Homomorphic Encryption

September 3, 2022

A few of us have been working with homomorphic encryption for a number of years now, but we never found a paper / book that covers all the foundational mathematical material in one place. So we decided to write one — well my postdoc Kelvin Yang Li decided to write one and Mike Purcell and I assisted … More A Tutorial Introduction to Lattice-based Cryptography and Homomorphic Encryption