CEA

Dynamic Distribution Shifts: OoD Detection with Dynamic Thresholds H/F

CEA  •  Onsite  •  3 days ago
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Job Description


Informations générales


Entité de rattachement


Le CEA est un acteur majeur de la recherche, au service des citoyens, de l'économie et de l'Etat.

Il apporte des solutions concrètes à leurs besoins dans quatre domaines principaux : transition énergétique, transition numérique, technologies pour la médecine du futur, défense et sécurité sur un socle de recherche fondamentale. Le CEA s'engage depuis plus de 75 ans au service de la souveraineté scientifique, technologique et industrielle de la France et de l'Europe pour un présent et un avenir mieux maîtrisés et plus sûrs.

Implanté au cœur des territoires équipés de très grandes infrastructures de recherche, le CEA dispose d'un large éventail de partenaires académiques et industriels en France, en Europe et à l'international.

Les 20 000 collaboratrices et collaborateurs du CEA partagent trois valeurs fondamentales :

• La conscience des responsabilités
• La coopération
• La curiosité


Référence


2025-38059

du poste


Domaine

Systèmes d'information


Contrat

Stage


Intitulé de l'offre

Dynamic Distribution Shifts: OoD Detection with Dynamic Thresholds H/F


Sujet de stage

Trustworthy Deep Learning - Confidence Monitoring


Durée du contrat (en mois)

3


Description de l'offre

The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented in the training data and can naturally arrive during deployment (i.e., a distribution shift), increasing the risk of obtaining wrong predictions. Consequently, OoD samples detection is crucial in safety-critical tasks, such as healthcare or automated vehicles, where trustworthy models are required.

The existing literature for the OoD detection problem focuses on the development of confidence scores where a threshold is applied to build a binary classifier to tell if a sample is in-distribution (InD) or OoD. In particular, the confidence score threshold is typically set using the values that correspond to InD samples, such that 95% of the confidence score values from InD samples fall above the selected thresholds, i.e., 95% True Positive Rate. However, setting a fixed threshold can lead to high False Positive Rate (FPR) values. In addition, even if the InD remains the same after deployment, the OoD could vary, resulting in FPR fluctuations. These two situations are of high interest in safety-critical applications, as misclassifying the confidence score value of an OoD sample as InD (False Positive) can result in more catastrophic consequences than misclassifying the confidence score value of an InD as OoD (False Negative).

To address the limitations and impact of a single fixed threshold selection, recent works propose using adaptive thresholds or a set of candidate thresholds to tackle the problem of dynamic distribution shifts. Specifically, in this internship position, we propose building on the work of Timans et al., who proposed a framework that leverages game theory and sequential hypothesis testing to assess the validity of a set of candidate thresholds. Therefore, the internship position aims to extend this work by exploring one or multiple of the following directions of improvement:

  • Dynamic threshold selection (vs. fixed thresholds)
  • Adaptive betting strategies (vs. static betting strategy)
  • Adaptive windowing/batching (vs. fixed windows/batches size)
  • Game theory methods: e.g., use of market-making algorithms (for threshold selection, and finding the optimal size of windows/batches)


Moyens / Méthodes / Logiciels

Statistics, Game Theory


Profil du candidat

  • Master students (M1/M2 – France)
  • Proficiency in Python, NumPy, SciPy, sciki-tlearn, PyTorch,…
  • Solid background in math, probability & statistics

Localisation du poste


Site

Saclay


Localisation du poste

France, Ile-de-France, Essonne (91)


Ville


Palaiseau

Critères candidat


Langues

Anglais (Courant)


Diplôme préparé

Bac - Baccalauréat général


Formation recommandée

Math, Statistics


Possibilité de poursuite en thèse

Oui

CEA

About CEA

The CEA is the French Alternative Energies and Atomic Energy Commission ("Commissariat à l'énergie atomique et aux énergies alternatives"​). It is a public body established in October 1945 by General de Gaulle. A leader in research, development and innovation, the CEA mission statement has two main objectives: To become the leading technological research organization in Europe and to ensure that the nuclear deterrent remains effective in the future.

The CEA is active in four main areas: low-carbon energies, defense and security, information technologies and health technologies. In each of these fields, the CEA maintains a cross-disciplinary culture of engineers and researchers, building on the synergies between fundamental and technological research.

The civilian programs of the CEA received 49% of their funding from the French government, and 30% from external sources (partner companies and the European Union).

The CEA had a budget of 4,3 billion euros.

The CEA is based in ten research centers in France, each specializing in specific fields. The laboratories are located in the Paris region, the Rhône-Alpes, the Rhône valley, the Provence-Alpes-Côte d'Azur region, Aquitaine, Central France and Burgundy. The CEA benefits from the strong regional identities of these laboratories and the partnerships forged with other research centers, local authorities and universities.

Industry
Biotech & Life Sciences
Company Size
10,000+ employees
Headquarters
Paris, FR
Year Founded
Unknown
Website
cea.fr
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