11 citations · 16 across the 18 of their papers we have counts for
9 papers · 1 filter
Prophet and Secretary at the Same Time
Gregory Kehne, Thomas Kesselheim
Many online problems are studied in stochastic settings for which inputs are samples from a known distribution, given in advance, or from an unknown distribution. Such distribution…
An Efficient Algorithm for Minimizing Ordered Norms in Fractional Load Balancing
Daniel Blankenburg, Antonia Ellerbrock, Thomas Kesselheim +1
We study the problem of minimizing an ordered norm of a load vector (indexed by a set of resources), where a finite number of customers contribute to the load of each r…
Integral Online Algorithms for Set Cover and Load Balancing with Convex Objectives
Thomas Kesselheim, Marco Molinaro, Kalen Patton +1
Online Set Cover and Load Balancing are central problems in online optimization, and there is a long line of work on developing algorithms for these problems with convex objectives…
Supermodular Approximation of Norms and Applications
Thomas Kesselheim, Marco Molinaro, Sahil Singla
Many classical problems in theoretical computer science involve norm, even if implicitly; for example, both XOS functions and downward-closed sets are equivalent to some norms. The…
Approximating Optimum Online for Capacitated Resource Allocation
Alexander Braun, Thomas Kesselheim, Tristan Pollner +1
We study online capacitated resource allocation, a natural generalization of online stochastic max-weight bipartite matching. This problem is motivated by ride-sharing and Internet…
Online and Bandit Algorithms Beyond Norms
Thomas Kesselheim, Marco Molinaro, Sahil Singla
Vector norms play a fundamental role in computer science and optimization, so there is an ongoing effort to generalize existing algorithms to settings beyond and $\el…