Description du projet
Résoudre le problème de l’effet boîte noire pour l’apprentissage automatique
La croissance et les progrès récents des technologies d’apprentissage automatique (AA) ont apporté des avantages considérables et permit des interactions avec divers secteurs, ayant un impact sur les systèmes autonomes, la prise de décision, la découverte scientifique et le diagnostic médical. Cependant, à mesure que les systèmes prédictifs de l’AA se font plus sophistiqués, leur raisonnement devient souvent plus difficile à interpréter, ce qui soulève des inquiétudes concernant leur sécurité. Le projet SafetyBounds, financé par le CER, entend s’attaquer à ce problème de sécurité, communément appelé «effet boîte noire». Plus précisément, il établira des limites d’erreur interprétables et précises sur les prédictions de l’AA, à partir desquelles de précieuses informations peuvent être tirées. À terme, le projet jettera les bases des futures pratiques de conception de l’AA et de ses systèmes d’apprentissage.
Objectif
Recent breakthroughs in machine learning (ML) have brought about a transformative impact on decision-making, autonomous systems, medical diagnosis, and creation of new scientific knowledge. However, this progress has a major drawback: modern predictive systems are extremely complex and hard to interpret, a problem known as the black-box effect. The opaque nature of modern ML models, trained on increasingly diverse, incomplete, and noisy data, and later deployed in varying environments, hinders our ability to comprehend what drives inaccurate predictions, biased outcomes, and test time failures. Perhaps the most pressing question of our times is this: can we trust the predictions for future unseen instances obtained by black-box systems? The lack of practical guarantees on the limits of predictive performance poses a significant obstacle to deploying ML in applications that affect people's lives, opportunities, and science.
My overarching goal is to put precise, interpretable, and robust error bounds on ML predictions, communicating rigorously what can be honestly inferred from data. I call for the development of protective ecosystems that can be seamlessly plugged into any ML model to monitor and guarantee its safety.
This proposal introduces a unique interplay between statistics--the grammar of science--and ML--the art of learning from experience. Leveraging my expertise in both domains, I will show how statistical methodologies such as conformal prediction and test-martingales can empower ML, and how recent breakthroughs in ML such as semi-supervised learning and domain adaptation technologies can empower statistics. I will tackle challenges rooted in real-world problems concerning (1) availability and (2) quality of training data, as well as (3) test-time drifting data.
A successful outcome would not only lead to a timely and rigorous way toward safe ML, but may also significantly reform the way we develop, deploy, and interact with learning systems.
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Appel à propositions
(s’ouvre dans une nouvelle fenêtre) ERC-2024-STG
Voir d’autres projets de cet appelRégime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
32000 Haifa
Israël