arXiv Machine Learning

Graph Partitioning with Demands: Generalized Conductance and its Applications

arXiv:2607. 13218v1 Announce Type: cross Abstract: In this work, we study various graph partitioning problems under a general demand model.

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
Jul 3

Incremental (k, z)-Clustering on Graphs

arXiv:2602. 08542v3 Announce Type: replace-cross Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center.

By Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
arXiv Machine Learning
Jun 19

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.

By Richard Yi Da Xu
arXiv Machine Learning
Jun 8

The Proxy Benders Decomposition

arXiv:2606. 07403v1 Announce Type: cross Abstract: Benders decomposition is a fundamental framework for solving large-scale mixed-integer optimization problems with complicating variables that, when fixed, yield significantly easier subproblems.

By Changkun Guan, El Mehdi Er Raqabi, Mathieu Tanneau, Pascal Van Hentenryck