Category: Data Science Education

On the Semantics of Differential Privacy and Its Responsible Use

September 15, 2025

Differential Privacy (DP) is one of the most widely adopted formal model of privacy protection but its semantics, especially in the presence of correlated data and in the adversarial interactive setting, is still not broadly understood among data science practitioners. In this paper, we first look at how DP originated from research on database-reconstruction attacks … More On the Semantics of Differential Privacy and Its Responsible Use

Update on Social Cost of Multi-Agent Reinforcement Learning Paper

April 24, 2025

I recently released on arXiv a new version of the paper The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis, which can be found at https://arxiv.org/abs/2412.02091 The new version has These are all non-trivial extensions of the paper that build on recent new results in different fields and they are worth … More Update on Social Cost of Multi-Agent Reinforcement Learning Paper

Algebraic Intuitions behind Fourier Transforms

February 7, 2025

There’s been a lot written about Fourier Transforms over the years. From a physics perspective, I would recommend the Harmonics chapter in the Feynman Lectures on Physics. I also found Elan Ness-Cohn’s visual explanation of Fourier Transforms really satisfying. In the notes linked below, I have tried to give, in 5 short pages, the intuition … More Algebraic Intuitions behind Fourier Transforms

A Simplistic Guide to Using Fairness Criteria in Machine Learning

January 10, 2025

Fairness in Machine Learning is a topic that I have been wanting to better understand for a little while now, and this blog post summarises what I learned from reading the Fairness and Machine Learning (FML) book by Solon Barocas, Moritz Hardt and Arvind Narayanan available at https://fairmlbook.org these past couple of days. (The book … More A Simplistic Guide to Using Fairness Criteria in Machine Learning

The Problem of Social Cost in Multi-Agent Universal Reinforcement Learning

December 5, 2024

While I have worked on aspects of AI safety for quite a few years now, in particular privacy technologies and confidential computing, I am a late convert on the importance of Artificial General Intelligence (AGI) safety research and did not take the problem seriously until about 1 year ago. My mindset has now changed completely … More The Problem of Social Cost in Multi-Agent Universal Reinforcement Learning

Approximating Solomonoff Induction

November 23, 2024

As is well-known by now, the universal AI agent AIXI is made up of two key components: Solomonoff Induction for universal sequential prediction, and expectimax search for planning. There are several proposed and reasonably effective approximations of the Solomonoff Induction component using the factored, binarised Context Tree Weighting algorithm [WST95, VNHUS09] and its generalisation to … More Approximating Solomonoff Induction

Natural Exponential Functions in Inequalities

October 16, 2024

Have you ever wondered why the natural exponential function shows up so frequently in mathematical inequalities? Here’s a graph of the natural exponential function. The constant e has a special place in mathematics, which is beautifully chronicled in Eli Maor’s book [M94]. The definition of e that is most useful and intuitive for our purpose … More Natural Exponential Functions in Inequalities

Dealing with Linkage Attacks using Differential Privacy

September 13, 2024

A key claim of differential privacy in [DR14] is that it provides “automatic neutralization of linkage attacks, including all those attempted with all past, present, and future datasets and other forms and sources of auxiliary information”. This is an important and often repeated claim — see e.g. [N17, Section E] and [PR23] — but the … More Dealing with Linkage Attacks using Differential Privacy

Privacy Technologies for Financial Intelligence

August 20, 2024

It took a little while to write, but hopefully the following survey paper by Yang Li, Thilina Ranbaduge and yours truly can help demystify financial intelligence and privacy technologies for practitioners and technologists alike. The focus is on anti-money laundering and counter-terrorism financing, but the opportunity set is much broader. https://arxiv.org/abs/2408.09935 Here’s the abstract of … More Privacy Technologies for Financial Intelligence