Probability and Machine Learning

Abstract: In this talk, I will challenge common ideas about probability, particularly in the context of machine learning. The main point is that there are no correct probabilities, that they are always constructed rather than discovered. I will show that constructed probabilities can still be useful and specify the assumptions under which e.g. expected utility maximisation is a sensible policy (which is usually taken for granted). Based on these general considerations, I will outline some insights for algorithmic fairness. As for other parts of my talk, central aspects are the tension between individuals and groups as well as a general notion of calibration. I will then draw on previously discussed issues to criticise the way individual predictions are typically presented in machine learning. In the last part, I will argue against the true distribution framework that underlies almost every analysis of theoretical guarantees in machine learning.

Short bio: Ben holds Bachelor’s degrees in Mathematics and Philosophy from LMU Munich as well as Master’s degrees in Philosophy of Science from LMU and in Computer Science from the University of Oxford. He continued to work there as a research assistant with the OATML group before starting a PhD at the University of Tübingen with Bob Williamson. Ben is co-supervised by Nuria Oliver within the ELLIS programme and will spend the winter semester 2024/25 in Alicante. His main interests lie at the intersection between the social impact of algorithmic predictions and the foundations of machine learning and probability.

Presenter: Benedikt Höltgen

Date: 2024-02-14 11:00 (CET)

Location: Oficinas ELLIS Alicante, Muelle Pte., 5 – Edificio A, Alicante 03001, Alicante ES