Neurosymbolic Conformal Classification
Arthur Ledaguenel, C\'eline Hudelot, Mostepha Khouadjia

TL;DR
This paper explores the integration of neurosymbolic AI and conformal prediction to enhance trustworthiness in machine learning by providing statistical guarantees and combining reasoning with neural learning.
Contribution
It introduces neurosymbolic conformal prediction techniques, demonstrating how these methods can complement each other to improve confidence guarantees in AI systems.
Findings
Multiple neurosymbolic conformal prediction methods are proposed.
The techniques vary in confidence set size and computational complexity.
The approaches are distribution-free and model-agnostic.
Abstract
The last decades have seen a drastic improvement of Machine Learning (ML), mainly driven by Deep Learning (DL). However, despite the resounding successes of ML in many domains, the impossibility to provide guarantees of conformity and the fragility of ML systems (faced with distribution shifts, adversarial attacks, etc.) have prevented the design of trustworthy AI systems. Several research paths have been investigated to mitigate this fragility and provide some guarantees regarding the behavior of ML systems, among which are neurosymbolic AI and conformal prediction. Neurosymbolic artificial intelligence is a growing field of research aiming to combine neural network learning capabilities with the reasoning abilities of symbolic systems. One of the objective of this hybridization can be to provide theoritical guarantees that the output of the system will comply with some prior…
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Taxonomy
TopicsMorphological variations and asymmetry
MethodsSparse Evolutionary Training
