I am sometimes asked what is the difference between Machine Learning (ML) and X, where X is one of a number of things like Statistics, Evolutionary Computing, Control Theory, etc. A variation of the question is what are problem classes that can be tackled by both ML and non-ML techniques, and what are the pros … More Machine Learning: A Broad Church
Boredom is the mother of creativity: fun that doesn’t cost a cent. Teddy bear logic: A breath of fresh air. An unexpected victim. The Anzac spirit lives on.
My former postdoc Lingjuan Lyu has been working with a few research collaborators on a fair and privacy-preserving federated deep-learning framework and a paper describing the framework has just been published at the IEEE Transactions on Parallel and Distributed Systems. Here’s the paper details: Title: Towards Fair and Privacy-Preserving Federated Deep Models Abstract: The current … More Towards Fair and Privacy-Preserving Federated Deep Learning Models
When the GFC happened, I was managing so little money the whole thing was a non-event for me. Sure enough, I experienced swings in emotion but my losses and gains from that period barely registered in my current balance sheet. So going into this crisis, I was determined to make it count. I have made … More Have You Been Panic Buying?
Effective decision-making is one of those skills that separates good leaders from the more ordinary leaders. In this post, I summarise a few lessons I learned from good leaders I have had the privilege to work for in the past. These decision-making principles have served me well in the last couple of years as I … More Some Decision-Making Principles
I was recently asked a question on what are the weaknesses of Agile as a framework, and what do we have to do to work around those weaknesses. This is a real if somewhat unusual question, and I can imagine many people who grew up with traditional waterfall project-management methodologies asking that question from time … More Agile, Waterfall, Wagile, and All That
Another day, another paper, this time by my postdoc Lingjuan Lyu and a few collaborators. Here’s the abstract: In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a … More Distributed Privacy-Preserving Prediction
A few ANU colleagues and I have just completed a paper on a suffix-tree-based algorithm for computing the longest common substring of two strings in a privacy-preserving manner. Here’s the abstract: The task of calculating similarities between strings held by different organizations without revealing these strings is an increasingly important problem in areas such as … More Accurate and Efficient Privacy-Preserving String Matching
The Networks scientist Albert-Laszlo Barabasi’s latest book The Formula: The Science Behind Why People Succeed or Fail is a cracker. His six laws of success summarise more than a decade of research into the science of success, in particular how the social network in which we live and operate, with its many kinds of relationships … More The Success Formula