Séminaire général

Résumé : 
The mathematical theory of machine learning offers formal guarantees to
support the epistemological claim that our standard machine learning
algorithms are, in fact, good learning algorithms. This is a modern
version of the traditional project in the philosophy of science to
provide a formal justification for scientific or inductive inferences.
But both in philosophy and in machine learning there also exist
well-known skeptical results and arguments against the very possibility
of inductive justification. This raises the question how a positive
story of justification, and in particular a mathematical theory of
generalization in machine learning, can exist at all.

In this talk, I answer this question by spelling out the kind of
justification that the theory of machine learning offers: general and
analytic, yet model-relative. I will argue that this kind of
justification fits in a broader epistemological perspective on inquiry.
Finally, I will briefly address the recent debate about the apparent
failure of classical machine learning theory to explain the
generalization of modern algorithms like deep neural networks, and the
epistemological contours of a new theory.

Organisation : Marion Vorms et Philippe Huneman