Publications related to Optimal Risk Sharing with Different Reference Probabilities

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, Wolfram Wiesemann

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

2022

Active learning for multi-objective optimization of processes and energy systems

François Maréchal, Julia Granacher

In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not ...

Elsevier Science2022

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

Extracting the Critical Rooting Length in Plant Uprooting by Flow From Pullout Experiments

Paolo Perona, Giulio Calvani

The growth and establishment of riparian vegetation on river bedforms is of hydrological as well as ecological importance as it helps in enhancing spatial heterogeneity and thus the biodiversity of river corridors. Yet, during floods, flow drag and scourin ...

AGU2019

MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring

Jan Sickmann Hesthaven, Caterina Bigoni

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offine-online decompo ...

MATHICSE2019

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

EPFL2018

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Pascal Fua, François Fleuret

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probabilit ...

Ieee2017

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Timm Faulwasser

The present contribution demonstrates the applicability of polynomial chaos expansion to stochastic (optimal) AC power flow problems that arise in the operation of power grids. For rectangular power flow, polynomial chaos expansion together with Galerkin p ...

IEEE2016

Systems and methods for tracking interacting objects

Pascal Fua, François Fleuret, Engin Türetken, Xinchao Wang

Systems and methods for tracking interacting objects may acquire, with a sensor, and two or more images associated with two or more time instances. A processor may generate image data from the two or more images. The processor may apply an extended Probabi ...

2015

Optimal Risk Sharing with Different Reference Probabilities

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On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Active learning for multi-objective optimization of processes and energy systems

Statistical Aspects of Wasserstein Distances

Extracting the Critical Rooting Length in Plant Uprooting by Flow From Pullout Experiments

MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Systems and methods for tracking interacting objects

Active learning for multi-objective optimization of processes and energy systems

Statistical Aspects of Wasserstein Distances

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

Extracting the Critical Rooting Length in Plant Uprooting by Flow From Pullout Experiments

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Systems and methods for tracking interacting objects

MATHICSE Technical Report: Simulation-Based Anomaly Detection and Damage Localization: an Application to Structural Health Monitoring