Publications related to Assessment of the Lightning Performance of overhead distribution lines based on Lightning Location Systems data

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

Minimax rate for optimal transport regression between distributions

Victor Panaretos, Laya Ghodrati

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

ELSEVIER2022

Comparing Annual Glare Simulation Methods

Stephen William Wasilewski

Recently, there have been multiple proposals for faster methods to calculate glare metrics, daylight glare probability (DGP) in particular. This is driven simultaneously by the lengthy times required to simulate DGP with a conventional image-based approach ...

2022

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Sylvain Calinon, Emmanuel Pignat

Probability distributions are key components of many learning from demonstration (LfD) approaches, with the spaces chosen to represent tasks playing a central role. Although the robot configuration is defined by its joint angles, end-effector poses are oft ...

SAGE PUBLICATIONS LTD2021

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Kilian Schindler

Stochastic optimization is a popular modeling paradigm for decision-making under uncertainty and has a wide spectrum of applications in management science, economics and engineering. However, the stochastic optimization models one faces in practice are int ...

EPFL2020

Distributional Robustness in Mechanism Design

Cagil Kocyigit

Mechanism design theory examines the design of allocation mechanisms or incentive systems involving multiple rational but self-interested agents and plays a central role in many societally important problems in economics. In mechanism design problems, agen ...

EPFL2020

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

or

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

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

Global turbulence features across marginality and non-local pedestal-core interactions

Olivier Sauter, Laurent Villard, Stephan Brunner, Emmanuel Lanti, Alberto Bottino, Ben McMillan, Noé Thomas Elie Ohana

Spatially non-local aspects of turbulent transport in tokamak plasmas are examined with global gyrokinetic simulations using the ORB5 code. Inspired by very accurate measurements in the TCV tokamak in L-mode, we initialise plasma profiles with constant log ...

2019

Assessment of the Lightning Performance of overhead distribution lines based on Lightning Location Systems data

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

Minimax rate for optimal transport regression between distributions

Comparing Annual Glare Simulation Methods

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Wasserstein Distributionally Robust Learning

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Distributional Robustness in Mechanism Design

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Statistical Aspects of Wasserstein Distances

Global turbulence features across marginality and non-local pedestal-core interactions

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

Wasserstein Distributionally Robust Learning

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

Statistical Aspects of Wasserstein Distances

Minimax rate for optimal transport regression between distributions

Learning from demonstration using products of experts: Applications to manipulation and task prioritization

Comparing Annual Glare Simulation Methods

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Global turbulence features across marginality and non-local pedestal-core interactions

Distributional Robustness in Mechanism Design