1 citations · 2 across the 10 of their papers we have counts for
15 papers · 1 filter
Better Late Than Never: Online Flow Time Scheduling with Online Estimates
Anupam Gupta, Haim Kaplan, Alexander Lindermayr +2
In the classical online flow-time scheduling problem on a single machine, jobs arrive over time and must be processed to minimize the total time they spend in the system: for over…
Correlation Clustering with Random Partial Information
Rajath Rao K. N., Jens Schlöter, Sami Davies +2
Correlation clustering is a fundamental unsupervised learning problem. On complete graphs, both the min-disagreement and min-max objectives admit constant-factor approximations, ye…
Online Scheduling with a Stochastic Signal
Romain Cosson, Jingwei Li, Alexander Lindermayr +1
Nonclairvoyant scheduling is a fundamental online model in which processing times are initially unknown to the scheduler. Unfortunately, for important objectives such as total comp…
A Simpler Analysis for -Clairvoyant Flow Time Scheduling
Anupam Gupta, Haim Kaplan, Alexander Lindermayr +2
We simplify the proof of the optimality of the Shortest Lower-Bound First (SLF) algorithm, introduced by Gupta, Kaplan, Lindermayr, Schlöter, and Yingchareonthawornchai [FOCS'25],…
Non-Clairvoyant Scheduling with Progress Bars
Ziyad Benomar, Romain Cosson, Alexander Lindermayr +1
In non-clairvoyant scheduling, the goal is to minimize the total job completion time without prior knowledge of individual job processing times. This classical online optimization…
Competitive Query Minimization for Stable Matching with One-Sided Uncertainty
Evripidis Bampis, Konstantinos Dogeas, Thomas Erlebach +3
We study the two-sided stable matching problem with one-sided uncertainty for two sets of agents A and B, with equal cardinality. Initially, the preference lists of the agents in A…
Accelerating Matroid Optimization through Fast Imprecise Oracles
Franziska Eberle, Felix Hommelsheim, Alexander Lindermayr +3
Querying complex models for precise information (e.g. traffic models, database systems, large ML models) often entails intense computations and results in long response times. Thus…
Santa Claus meets Makespan and Matroids: Algorithms and Reductions
Étienne Bamas, Alexander Lindermayr, Nicole Megow +2
In this paper we study the relation of two fundamental problems in scheduling and fair allocation: makespan minimization on unrelated parallel machines and max-min fair allocation,…
Sorting and Hypergraph Orientation under Uncertainty with Predictions
Thomas Erlebach, Murilo Santos de Lima, Nicole Megow +1
Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of unce…
Minimalistic Predictions to Schedule Jobs with Online Precedence Constraints
Alexandra Lassota, Alexander Lindermayr, Nicole Megow +1
We consider non-clairvoyant scheduling with online precedence constraints, where an algorithm is oblivious to any job dependencies and learns about a job only if all of its predece…
Set Selection under Explorable Stochastic Uncertainty via Covering Techniques
Nicole Megow, Jens Schlöter
Given subsets of uncertain values, we study the problem of identifying the subset of minimum total value (sum of the uncertain values) by querying as few values as possible. This s…
Learning-Augmented Query Policies for Minimum Spanning Tree with Uncertainty
Thomas Erlebach, Murilo Santos de Lima, Nicole Megow +1
We study how to utilize (possibly erroneous) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. Our aim is to minimize the number…
Orienting (hyper)graphs under explorable stochastic uncertainty
Evripidis Bampis, Christoph Dürr, Thomas Erlebach +3
Given a hypergraph with uncertain node weights following known probability distributions, we study the problem of querying as few nodes as possible until the identity of a node wit…
Learning-Augmented Query Policies
Thomas Erlebach, Murilo S. de Lima, Nicole Megow +1
We study how to utilize (possibly machine-learned) predictions in a model for computing under uncertainty in which an algorithm can query unknown data. The goal is to minimize the…
Fully Dynamic Algorithms for Knapsack Problems with Polylogarithmic Update Time
Franziska Eberle, Nicole Megow, Lukas Nölke +2
Knapsack problems are among the most fundamental problems in optimization. In the Multiple Knapsack problem, we are given multiple knapsacks with different capacities and items wit…