7 citations · 10 across the 6 of their papers we have counts for
6 papers
Fast Feature Selection with Fairness Constraints
Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch +4
We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy al…
Adaptive Sampling for Fast Constrained Maximization of Submodular Function
Francesco Quinzan, Vanja Doskoč, Andreas Göbel +1
Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often invo…
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings
Vanja Doskoč, Tobias Friedrich, Andreas Göbel +3
We study the problem of maximizing a non-monotone submodular function under multiple knapsack constraints. We propose a simple discrete greedy algorithm to approach this problem, a…
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints
Tobias Friedrich, Andreas Göbel, Frank Neumann +2
We investigate the performance of a deterministic GREEDY algorithm for the problem of maximizing functions under a partition matroid constraint. We consider non-monotone submodular…
Evolutionary Algorithms and Submodular Functions: Benefits of Heavy-Tailed Mutations
Tobias Friedrich, Andreas Göbel, Francesco Quinzan +1
A core feature of evolutionary algorithms is their mutation operator. Recently, much attention has been devoted to the study of mutation operators with dynamic and non-uniform muta…
Approximating Optimization Problems using EAs on Scale-Free Networks
Ankit Chauhan, Tobias Friedrich, Francesco Quinzan
It has been observed that many complex real-world networks have certain properties, such as a high clustering coefficient, a low diameter, and a power-law degree distribution. A ne…