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
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10 Nov 2026, 12h At Inria Saclay - Amphi Sophie Germain
Stephan Alaniz-Explanation by Default, Fallible in Practice: Hallucinations and Bias in Multimodal Foundation Models
Multimodal Foundation Models have emerged across an increasing number of vision-language applications. Because they generate fluent text alongside visual representations, these systems offer natural explanations by default. At the same time, this coherence can obscure important model limitations. In this talk, I explore the opportunities of generative explanations, then focus on two failure modes: hallucinations and social biases. Finally, I outline prospective directions toward more robust expl
Multimodal Foundation Models have emerged across an increasing number of vision-language applications. Because they generate fluent text alongside visual representations, these systems offer natural explanations by default. At the same time, this coherence can obscure important model limitations. In this talk, I explore the opportunities of generative explanations, then focus on two failure modes: hallucinations and social biases. Finally, I outline prospective directions toward more robust explanations.
Hugo Cui-Active learning in high dimensions: budget tradeoffs and optimal policies
In several domains (such as medical diagnostics, rare languages or scientific simulations) the cost of data annotation is high, and thus only a limited number of samples can be labeled and used for training. In such applications, it is instrumental to find, among a pool or a stream of unlabeled data, the most informative samples to label. We focus on linear classification, under Gaussian design, in the high-dimensional regime where the data dimension and the number of unlabeled samples grow pro
In several domains (such as medical diagnostics, rare languages or scientific simulations) the cost of data annotation is high, and thus only a limited number of samples can be labeled and used for training. In such applications, it is instrumental to find, among a pool or a stream of unlabeled data, the most informative samples to label. We focus on linear classification, under Gaussian design, in the high-dimensional regime where the data dimension and the number of unlabeled samples grow proportionally. In the pool-based case, for small-margin selection policies, we derive a sharp asymptotic characterization of the test error. We uncover a fundamental tradeoff in how the labeling budget should be allocated across the different selection steps. In the online setting, we determine analytically and numerically the selection policies that minimize the cumulative cost of labeling and mistakes, or maximize the final accuracy. We then comment on the case of two-layer networks. Based on joint works with Yue M Lu, Francesca Mignacco and Francesco Mori.
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01 Dec 2026, 12h At Inria Saclay - Amphi Sophie Germain
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:
