arXiv Machine Learning By Michael Benedikt, Alessio Mansutti

How (and when) can you fit examples to logic-based hypothesis classes over infinite structures?

Read the original on arXiv Machine Learning →

arXiv:2606. 01107v1 Announce Type: cross Abstract: We study fitting problems, sometimes called ``training problems'', where we have a finite sample consisting of inputs and outputs, and we want to know whether there is a function in a certain class that could produce these outputs, exactly or approximately, on the given inputs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Jul 24

Representative Sets in Propositional Abduction

arXiv:2607. 21183v1 Announce Type: cross Abstract: The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given manifestation.

By Johannes Schmidt (J\"onk\"oping University), Mohamed Maizia (J\"onk\"oping University, Link\"oping University), Victor Lagerkvist (Link\"oping University), Johannes K. Fichte (Link\"oping University)