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

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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

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

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

Graph Chatbot

Chattez avec Graph Search

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

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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

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

Wasserstein Distributionally Robust Learning

A Wasserstein-based measure of conditional dependence

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

Mathematical Foundations of Robust and Distributionally Robust Optimization

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

Quantum conformance test

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