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Stochastic approximation methods for PDE constrained optimal control problems with uncertain parameters

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Covid-19 contact tracing: efficacy and privacy

This article assesses the risk-risk trade-off between privacy and efficacy that the use of contact tracing apps entails. It argues for the use of privacy-preserving apps, but highlights potential weaknesses and cautions against allowing digital tracing to ...

EPFL2020

A Data-Driven Approach Based on Multivariate Copulas for Quantitative Risk Assessment of Concrete Dam

Zhenzhu Meng, Yating Hu, Chenfei Shao

Risk assessment of dam's running status is an important part of dam management. A data-driven method based on monitored displacement data has been applied in risk assessment, owing to its easy operation and real-time analysis. However, previous data-driven ...

MDPI2019

Spatial Risk Measures and Rate of Spatial Diversification

Erwan Fabrice Koch

An accurate assessment of the risk of extreme environmental events is of great importance for populations, authorities and the banking/insurance/reinsurance industry. Koch (2017) introduced a notion of spatial risk measure and a corresponding set of axioms ...

2019

"Dice"-sion Making under Uncertainty: When Can a Random Decision Reduce Risk?

Daniel Kuhn, Wolfram Wiesemann

Stochastic programming and distributionally robust optimization seek deterministic decisions that optimize a risk measure, possibly in view of the most adverse distribution in an ambiguity set. We investigate under which circumstances such deterministic de ...

2019

A central limit theorem for functions of stationary max-stable random fields on R-d

Erwan Fabrice Koch, Clément Dombry

Max-stable random fields are very appropriate for the statistical modelling of spatial extremes. Hence, integrals of functions of max-stable random fields over a given region can play a key role in the assessment of the risk of natural disasters, meaning t ...

ELSEVIER2019

Dynamic Average Diffusion With Randomized Coordinate Updates

Ali H. Sayed, Bicheng Ying, Kun Yuan

This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observa ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Multilevel Monte Carlo Approximation of Functions

Fabio Nobile, Sebastian Krumscheid

Many applications across sciences and technologies require a careful quantification of nondeterministic effects to a system output, for example, when evaluating the system’s reliability or when gearing it towards more robust operation conditions. At the he ...

2018

Energy and Reserve Dispatch with Distributionally Robust Joint Chance Constraints

Daniel Kuhn, Viet Anh Nguyen, Christos Ordoudis

We develop a two-stage stochastic program for energy and reserve dispatch, which ensures the safe operation of a power system with a high penetration of renewables and a strong interdependence with the natural gas system. Distributionally robust joint chan ...

2018

Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, Wolfram Wiesemann

We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed ...

2018

Robust Portfolio Optimization

Zheng Wei Yap

Since the 2008 Global Financial Crisis, the financial market has become more unpredictable than ever before, and it seems set to remain so in the forseeable future. This means an investor faces unprecedented risks, hence the increasing need for robust port ...

EPFL2017