2 citations · 2 across the 7 of their papers we have counts for
8 papers · 1 filter
From Estimates to Schedules: Learning-Augmented Restricted Assignment
Michalis Xefteris
In this work, we study Restricted Assignment scheduling on multiple machines, where each job can be processed only on a specified subset of machines and the objective is to minimiz…
Polynomial Time Learning-Augmented Algorithms for NP-hard Permutation Problems
Evripidis Bampis, Bruno Escoffier, Dimitris Fotakis +2
We consider a learning-augmented framework for NP-hard permutation problems. The algorithm has access to predictions telling, given a pair of elements, whether is before…
Improved FPT Approximation for Non-metric TSP
Evripidis Bampis, Bruno Escoffier, Michalis Xefteris
In the Traveling Salesperson Problem (TSP) we are given a list of locations and the distances between each pair of them. The goal is to find the shortest possible tour that visits…
Parsimonious Learning-Augmented Approximations for Dense Instances of -hard Problems
Evripidis Bampis, Bruno Escoffier, Michalis Xefteris
The classical work of (Arora et al., 1999) provides a scheme that gives, for any , a polynomial time approximation algorithm for dense instances of a family of $\mathcal…
Learning-Augmented Online TSP on Rings, Trees, Flowers and (almost) Everywhere Else
Evripidis Bampis, Bruno Escoffier, Themis Gouleakis +4
We study the Online Traveling Salesperson Problem (OLTSP) with predictions. In OLTSP, a sequence of initially unknown requests arrive over time at points (locations) of a metric sp…
The Covering Canadian Traveller Problem Revisited
Niklas Hahn, Michalis Xefteris
In this paper, we consider the -Covering Canadian Traveller Problem (-CCTP), which can be seen as a variant of the Travelling Salesperson Problem. The goal of -CCTP is fin…