Feb 12, 2021 · We consider this question from the (unexpected?) perspective of computable analysis. This allows us to define the computational tasks underlying verified ML in ...

Oct 22, 2023 · This paper is the first to formalise the concepts of classifiers and learners in ML in terms of computable analysis.

Most Machine Learning notions are based on real numbers. Computable Analysis is developed as the theory of functions on the real numbers and other sets from ...

Feb 12, 2021 · This allows us to define the computational tasks underlying verified ML in a model-agnostic way, and show that they are in principle computable.

Feb 15, 2021 · We define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning ...

The computational tasks underlying verified ML are defined in a model-agnostic way, and it is shown that they are in principle computable.

Jan 1, 2022 · It provides results about which properties of classifiers and learners are computable. By doing this we establish a bridge between the ...

It provides results about which properties of classifiers and learners are computable. By doing this we establish a bridge between the continuous mathematics ...

We define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning approaches ...

It is much less clear what verified machine learning should mean exactly. We consider this question from the (unexpected?) perspective of computable analysis.