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.

Summary generated by The Flow from the publisher's feed. 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)
arXiv Machine Learning
Jul 9

Is Randomness Necessary for Adaptive Data Analysis?

arXiv:2607. 07085v1 Announce Type: cross Abstract: The Adaptive Data Analysis (ADA) problem formalizes the challenge of preventing false discovery and overfitting when a dataset is repeatedly reused.

By Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Sapir, Uri Stemmer