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Graphical models strike back: non-linearity, actions, and calibration

CORDIS · observation · Publication date unknown

Graphical models strike back: non-linearity, actions, and calibration The recent success of machine learning (ML) has been fueled by black-box supervised learning on massive amounts of data. While this has led to many applications throughout science, engineering, and industry, new areas such as healthcare, transport, or robotics require interactions between predictions and decisions from high-dimensional data, such as in reinforcement learning (RL), and provably well-calibrated uncertainty estimates. This project aims to address these requirements with the same reliability and efficiency as supervised learning, which current RL algorithms do not possess. This will be achieved by opening the black box at an appropriate granularity and proposing a flexible, unified algorithmic framework based on probabilistic graphical models. The revival of this classical modeling tool presents significant, radically novel scientific challenges when dealing with high-dimensionality and nonlinea

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recordType

award

status

SIGNED

region

EU

value

2498920

unit

EUR