30 citations · 35 across the 11 of their papers we have counts for
5 papers
Data-driven Distributionally Robust Optimization over Time
Kevin-Martin Aigner, Andreas Bärmann, Kristin Braun +5
Stochastic Optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters…
Online Learning for Scheduling MIP Heuristics
Antonia Chmiela, Ambros Gleixner, Pawel Lichocki +1
Mixed Integer Programming (MIP) is NP-hard, and yet modern solvers often solve large real-world problems within minutes. This success can partially be attributed to heuristics. Sin…
Accelerated and Sparse Algorithms for Approximate Personalized PageRank and Beyond
David Martínez-Rubio, Elias Wirth, Sebastian Pokutta
It has recently been shown that ISTA, an unaccelerated optimization method, presents sparse updates for the -regularized personalized PageRank problem, leading to cheap ite…
Hierarchical Clustering via Spreading Metrics
Aurko Roy, Sebastian Pokutta
We study the cost function for hierarchical clusterings introduced by [arXiv:1510.05043] where hierarchies are treated as first-class objects rather than deriving their cost from p…
An efficient high-probability algorithm for Linear Bandits
Gábor Braun, Sebastian Pokutta
For the linear bandit problem, we extend the analysis of algorithm CombEXP from [R. Combes, M. S. Talebi Mazraeh Shahi, A. Proutiere, and M. Lelarge. Combinatorial bandits revisite…