arXiv:2608. 03706v1 Announce Type: new Abstract: Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence.
By Congwei Song
arXiv:2606. 01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution.
By Ignacio Boero, Ignacio Hounie, Luiz Chamon, Alejandro Ribeiro
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
By Hong Jun Jeon, Benjamin Van Roy
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data. The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science .
By Carolina Bento
arXiv:2607. 11947v1 Announce Type: cross Abstract: Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated.
By Yushi Hirose, Hiroo Irobe, Takafumi Kanamori
arXiv:2608. 04049v1 Announce Type: cross Abstract: The principle of Occam's razor, which instructs us to prefer simplicity in inductive inference, has attracted much scrutiny both in the philosophy of science and in machine learning.
By Tom F. Sterkenburg