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
In trying to set up a Data Products team recently, I quickly realised I don’t have a good working definition of what Data Products are or should be, at least not in the context I was operating in. Sure, a quick googling will surface many generic definitions of Data Products from respected sources like Forbes … More What Are Data Products?
I was recently asked to speak about my leadership brand, a topic I hadn’t seriously thought about ever, until now. So what is my brand. Here are a few things that I know: So I think my leadership brand is the rational leader who understands how things work, and who seeks to inspire his people … More My Leadership Brand
In this post on the last day of the year, I thought I will share a map of mathematical structures that are useful for thinking about knowledge representation and reasoning (KRR) issues in Artificial Intelligence and Machine Learning. It is built on top of the diagram shown in Max Tegmark’s paper Is “the theory of … More A Map of Mathematical Structures for AI
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 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
A few colleagues and I have just completed a new research paper titled Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces. The research took over 3 years and I am really excited about the underlying theory and its possible applications. In particular, the paper shows how we can lift’ Bayesian filtering to … More Bayesian Filtering on Structured Environments
Cyclotomic polynomials are widely used in the construction of lattice cryptography and homomorphic encryption schemes based on the Ring Learning With Error problem. In this short note, Kelvin Li and I attempt a self-contained introduction to the cyclotomic polynomials and the Galois groups of cyclotomic extensions.
Data matching and entity resolution is a common first step in data preparation and there is a thousand academic papers written on the subject in the literature. In practice, for large datasets – anything more than a million records will do as a definition of large here because most data-matching algorithms can’t handle that because … More A Note on Large Scale Data Matching and Entity Resolution
I read the book Divining a Digital Future: Mess and Mythology in Ubiquitous Computing by Genevieve Bell and Paul Dourish almost cover to cover last year and I have to say that finally gave me some understanding of the research agenda being pursued at the 3A Institute at the ANU. I hope I am not … More Book Review: Divining a Digital Future