1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Efficient Algorithms for Learning Monophonic Halfspaces in Graphs
Marco Bressan, Emmanuel Esposito, Maximilian Thiessen
We study the problem of learning a binary classifier on the vertices of a graph. In particular, we consider classifiers given by monophonic halfspaces, partitions of the vertices t…
cs.LG2024
A Theory of Interpretable Approximations
Marco Bressan, Nicolò Cesa-Bianchi, Emmanuel Esposito +3
Can a deep neural network be approximated by a small decision tree based on simple features? This question and its variants are behind the growing demand for machine learning model…
cs.DS2023★ 1 cited
Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees
Marco Bressan, Mauro Sozio
We give the first algorithm that maintains an approximate decision tree over an arbitrary sequence of insertions and deletions of labeled examples, with strong guarantees on the wo…