arXiv Machine Learning

Precedence-Constrained Decision Trees and Coverings

arXiv:2602. 21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them.

Hugging Face Trending Papers
Jul 14

Hierarchical $\mathcal{F}$-Clustering: Approximation and Hardness of Clustering into Trees and Bounded Diameter Graphs

Consider the following variation on the Hierarchical Clustering problem: Usually, while building a hierarchical clustering, one recursively partitions the data until each cluster becomes a singleton. We relax the halting condition of the recursive process to stop whenever the remaining cluster is a graph belonging to a class $\mathcal{F}$.

arXiv Machine Learning
Aug 7

Optimal or Greedy Decision Trees? Revisiting their Objectives, Tuning, and Performance

arXiv:2409. 12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditional approaches that locally optimize an impurity or information metric.

By Jacobus G. M. van der Linden, Dani\"el Vos, Mathijs M. de Weerdt, Sicco Verwer, Emir Demirovi\'c
arXiv Machine Learning
Jun 30

Actively Learning Halfspaces without Synthetic Data

arXiv:2509. 20848v2 Announce Type: replace-cross Abstract: In the classic point location problem, one is given an arbitrary dataset $X \subset \mathbb{R}^d$ of $n$ points with query access to an unknown halfspace $f : \mathbb{R}^d \to \{0,1\}$, and the goal is to learn the label of every point in $X$.

By Hadley Black, Kasper Green Larsen, Arya Mazumdar, Barna Saha, Geelon So
arXiv Machine Learning
4d ago

Local Search with Correlated Randomness

arXiv:2607.17469v2 Announce Type: replace-cross Abstract: How much does an algorithm's running-time distribution under independent randomness reveal about its behavior when independence is no longer...

By Yunbei Xu