SOURCE-LINKED INTELLIGENCE

Computational Foundations of Reliable Machine Learning

CORDIS · observation · Publication date unknown

Computational Foundations of Reliable Machine Learning Machine learning has seen huge advances in recent years, achieving super-human performance in complex tasks like image classification, text generation, and playing games. Despite this success, however, its applicability across industries is still limited as real-world data is often noisy, incomplete, or scarce, and reliability is paramount. To realize the full potential and extend the reach of machine learning, we need improved methods with performance guarantees that can reliably solve complex tasks under limited guidance and in the face of these real-world challenges. This project will develop such methods by focusing on establishing a rigorous theoretical framework for robust and efficient learning. Our research will center around two interconnected core themes: learning under noisy data and learning via interactive queries. The first theme will develop algorithms ca

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recordType

award

status

SIGNED

region

EU

value

1997500

unit

EUR