90 citations · 100 across the 6 of their papers we have counts for
7 papers
Statistical learning theory and Occam's razor: Regularization
Tom F. Sterkenburg
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 learnin…
Benign interpolation and Occam's razor
Tom F. Sterkenburg, Daniel A. Herrmann, Jan-Willem Romeijn
Contemporary deep learning methods generalize well even when they fit their training data perfectly, a phenomenon known as benign interpolation. This phenomenon cannot be accounted…
Solomonoff induction
Tom F. Sterkenburg
This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an att…
Statistical learning theory and Occam's razor: The core argument
Tom F. Sterkenburg
Statistical learning theory is often associated with the principle of Occam's razor, which recommends a simplicity preference in inductive inference. This paper distills the core a…
The no-free-lunch theorems of supervised learning
Tom F. Sterkenburg, Peter D. Grünwald
The no-free-lunch theorems promote a skeptical conclusion that all possible machine learning algorithms equally lack justification. But how could this leave room for a learning the…
On characterizations of learnability with computable learners
Tom F. Sterkenburg
We study computable PAC (CPAC) learning as introduced by Agarwal et al. (2020). First, we consider the main open question of finding characterizations of proper and improper CPAC l…
A generalized characterization of algorithmic probability
Tom F. Sterkenburg
An a priori semimeasure (also known as "algorithmic probability" or "the Solomonoff prior" in the context of inductive inference) is defined as the transformation, by a given unive…