FOSDEM 2026 highlights: security, LLMs, and software performance
Highlights and notes from FOSDEM 2026.
Machine learning
Empirically evaluating privacy in machine learning III: functional & gaussian differential privacy
The third in a series of blog posts on machine learning and privacy: why functional differential privacy is useful for auditing privacy in machine learning.
Empirically evaluating privacy in machine learning II: Hypothesis testing
The second in a series of blog posts on machine learning and privacy: Using statistics to think like a hacker, so we can avoid them!
Empirically evaluating privacy in machine learning I: Introduction
The first in a series of blog posts on machine learning and privacy.
Births are difficult to predict even with rich survey and full-population register data
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child within three years - through a near-ideal setting for prediction: a data …