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
The paper presents an order-theoretic characterization of consistent inductive inference for arbitrary binary hypothesis classes. It shows that consistency—making only finitely many prediction errors on any infinite sequence labeled by an unknown hypothesis—can be captured by a single linear order on finite realizable traces. This order must satisfy two conditions: conflicting traces select different least subtraces, and the order is well‑founded on traces of each fixed target, enabling a learner whose evidence decreases with each mistake. Conversely, any consistent learner induces such an order via canonical mistake transcripts and the Kleene–Brouwer ordering.
By Zhou Lu
arXiv:2501. 05844v4 Announce Type: replace Abstract: Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising computational techniques to reveal ``true'' causality.
By Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sir, Martin Krutsky
The article discusses how predictive benchmarking—evaluating machine learning models by their performance and ranking—serves as a core method in machine learning research. It argues that benchmark scores only reflect performance on specific datasets and learning problems, and that drawing broader scientific conclusions requires explicit assumptions. By adapting concepts from psychological validity theory, the authors propose validity conditions to make these assumptions clear, and demonstrate their application in two case studies (ImageNet and the Fragile Families Challenge) to illustrate how benchmark results can inform inferences about research progress and limits of predictability.
By Timo Freiesleben, Sebastian Zezulka
arXiv:2608. 03386v1 Announce Type: new Abstract: Contemporary deep learning methods generalize well even when they fit their training data perfectly, a phenomenon known as benign interpolation.
By Tom F. Sterkenburg, Daniel A. Herrmann, Jan-Willem Romeijn
arXiv:2606. 29346v1 Announce Type: new Abstract: Post-hoc explanation methods are routinely used to interpret scientific machine learning models, with the deliverable understood to be insight into the phenomenon the model has been trained on.
By Nick Oh, Helen Jin