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Multi-output conformal regression

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RCLW02 - Calibrating prediction uncertainty : statistics and machine learning perspectives

Conformal prediction allows us to construct distribution-free prediction regions with finite-sample coverage guarantees. While well-established in univariate settings, its extension to multi-output regression remains relatively underexplored and introduces additional challenges, such as capturing complex output dependencies and increased computational complexity. In this presentation, we introduce a unified comparative study of several conformal methods for multi-output prediction, including different classes of conformity scores. By integrating these methods within a common framework, we highlight key methodological differences and identify connections between them. We also compare their empirical performance across a range of real-world tabular regression datasets.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Average on weekdays: 8935.99820789 kWh Wordsworth Room, St John’s College Humanitarian Centre, c/o Fenner's, Gresham Rd, Cambridge CB1 2ES

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