About the seminar

This seminar aims to increase the links between the different laboratories in Saclay in the field of Applied Maths, Statistics and Machine Learning. The Seminar is organized every first Tuesday of the month with 2 presentations followed by a small refreshment. The localization of the seminar will change to accommodate the different labs.

Organization

Due to access restriction, you need to register for the seminar. A link is provided in the description and should also be sent with the seminar announcement. It will also help us organize for the food quantities. If you think you will come, please register! (even if you are unsure)

To not miss the next seminar, please subscribe to the announcement mailing list subscribe/palaisien.
You will receive email on the palaisien@inria.fr mailing list.
You can also add the calendar from the seminar to your own calendar (see below).

Next seminars

REGISTER 02 Jun 2026, 12h At Inria Saclay - Salle Gilles Kahn
Edouard Oyallon - TBA
TBA
TBA
Hélène HALCONRUY - Local transfer learning for nonparametric regression: An application to stock prediction
Transfer learning can improve prediction by leveraging related source tasks, but it may also suffer from negative transfer when similarities vary across the covariate space. In this work, we propose a nonparametric framework based on a local transfer assumption, where the target function is approximated locally by a simple transformation of the source. This allows similarity to vary across the space, encouraging useful transfer while avoiding harmful information sharing. In this talk, I will ...
Transfer learning can improve prediction by leveraging related source tasks, but it may also suffer
from negative transfer when similarities vary across the covariate space. In this work, we propose a
nonparametric framework based on a local transfer assumption, where the target function is
approximated locally by a simple transformation of the source. This allows similarity to vary across
the space, encouraging useful transfer while avoiding harmful information sharing.
In this talk, I will present the main theoretical results, including minimax rates showing that transfer
can alleviate the curse of dimensionality, as well as fully data-driven procedures to estimate both the
transfer and the target functions. I will conclude with an illustration on stock return prediction using
signature-based features.
This is joint work with Benjamin Bobbia and Paul Lejamtel.

Scientific Committee

The program and the organization of this seminar is driven by a scientific committee composed of members of the different laboratories in Saclay. The members of the committee are currently:

Funding

This seminar is made possible with financial support of the ENSAE and DataIA.