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

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Quantum conformance test

Mathematical Foundations of Robust and Distributionally Robust Optimization

A Wasserstein-based measure of conditional dependence

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

Ergodic Exploration Using Tensor Train: Applications in Insertion Tasks

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