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Phase Transitions in Group Testing

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K-Adaptability in Two-Stage Distributionally Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

We propose to approximate two-stage distributionally robust programs with binary recourse decisions by their associated K-adaptability problems, which pre-select K candidate second-stage policies here-and-now and implement the best of these policies once t ...

Elsevier Science Bv2016

A Comment on “Computational Complexity of Stochastic Programming Problems”

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Although stochastic programming problems were always believed to be computationally challenging, this perception has only recently received a theoretical justification by the seminal work of Dyer and Stougie (Mathematical Programming A, 106(3):423–432, 200 ...

Springer Heidelberg2016

K-Adaptability in Two-Stage Robust Binary Programming

Daniel Kuhn, Grani Adiwena Hanasusanto, Wolfram Wiesemann

Over the last two decades, robust optimization has emerged as a computationally attractive approach to formulate and solve single-stage decision problems affected by uncertainty. More recently, robust optimization has been successfully applied to multi-sta ...

Informs2015

Designing Augmented Reality for the Classroom

Pierre Dillenbourg, Sébastien Cuendet, Quentin Bonnard

Augmented reality (AR) has recently received a lot of attention in education. Multiple AR systems for learning have been developed and tested through empirical studies often conducted in lab settings. While lab studies can be insightful, they leave out the ...

Elsevier2013

MAV Urban Localization from Google Street View Data

Davide Scaramuzza

We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint c ...

2013

Blind Phase Calibration in Sparse Recovery

Rémi Gribonval, Gilles Puy

We consider a blind calibration problem in a compressed sensing measurement system in which each sensor introduces an unknown phase shift to be determined. We show that this problem can be approached similarly to the problem of phase retrieval from quadrat ...

2013

Graph-Based Information Processing

Amin Karbasi

We live in a world characterized by massive information transfer and real-time communication. The demand for efficient yet low-complexity algorithms is widespread across different fields, including machine learning, signal processing and communications. Mo ...

EPFL2012

On the online track assignment problem

Benjamin Leroy-Beaulieu

This paper aims to start an analytical study of the computational complexity of some online We analyze the following problem. Consider a train station consisting of a set of parallel tracks. Each track can be approached from one side only or from both side ...

2012

Distributed Group Testing Detection in Sensor Networks

Pascal Frossard, Tamara Tosic

We consider the problem of failure detection in sensor networks and we propose a new distributed detection algorithm based on Group Testing. We examine the presence of defective sensors by employing tests over locally gathered sensor measurements. Tests ar ...

2012

Strongly feasible stochastic model predictive control

Milan Korda

In this article we develop a systematic approach to enforce strong feasibility of probabilistically constrained stochastic model predictive control problems for linear discrete-time systems under affine disturbance feedback policies. Two approaches are pre ...

2011