Publications related to Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

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

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

Fabio Nobile, Sebastian Krumscheid, Sundar Subramaniam Ganesh

In this work, we consider the problem of estimating the probability distribution, the quantile or the conditional expectation above the quantile, the so called conditional-value-at-risk, of output quantities of complex random differential models by the MLM ...

2022

A Wasserstein-based measure of conditional dependence

Negar Kiyavash, Seyed Jalal Etesami, Kun Zhang

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines. This paper studies some shortcomings of the existing dependency measures in detecting direct causal influences or their lack of ability for group ...

2022

Quantum conformance test

Elena Losero, Marco Genovese

We introduce a protocol addressing the conformance test problem, which consists in determining whether a process under test conforms to a reference one. We consider a process to be characterized by the set of end products it produces, which is generated ac ...

AMER ASSOC ADVANCEMENT SCIENCE2021

Mathematical Foundations of Robust and Distributionally Robust Optimization

Daniel Kuhn, Jianzhe Zhen, Wolfram Wiesemann

Robust and distributionally robust optimization are modeling paradigms for decision-making under uncertainty where the uncertain parameters are only known to reside in an uncertainty set or are governed by any probability distribution from within an ambigu ...

2021

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Sylvain Calinon, Suhan Narayana Shetty

In robotics, ergodic control extends the tracking principle by specifying a probability distribution over an area to cover instead of a trajectory to track. The original problem is formulated as a spectral multiscale coverage problem, typically requiring t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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

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

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Andreas Loukas, Nikolaos Karalias

Combinatorial optimization (CO) problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning framework for CO problems on graphs that can provide integral solutions of ...

2020

On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments

Thomas Mountford, Kamran Kalbasi

In this paper, we study the local times of vector-valued Gaussian fields that are 'diagonally operator-selfsimilar' and whose increments are stationary. Denoting the local time of such a Gaussian field around the spatial origin and over the temporal unit h ...

INT STATISTICAL INST2020

Distributionally Robust Optimization with Polynomial Densities: Theory, Models and Algorithms

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

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

A Wasserstein-based measure of conditional dependence

Quantum conformance test

Mathematical Foundations of Robust and Distributionally Robust Optimization

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Wasserstein Distributionally Robust Learning

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments

Wasserstein Distributionally Robust Learning

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

Quantum conformance test

Quantifying uncertain system outputs via the multi-level Monte Carlo method --- distribution and robustness measures

A Wasserstein-based measure of conditional dependence

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Mathematical Foundations of Robust and Distributionally Robust Optimization

On the probability distribution of the local times of diagonally operator-self-similar Gaussian fields with stationary increments