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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

Scalable Stochastic Optimization: Scenario Reduction with Guarantees

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Chattez avec Graph Search

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

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

A Novel Decomposition Solution Approach for the Restoration Problem in Distribution Networks

Variational Inference with Mixture Model Approximation for Applications in Robotics

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Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Fair Colorful k-Center Clustering

Multimodal water age distributions and the challenge of complex hydrological landscapes

Distributionally Robust Mechanism Design

Redundant features can hurt robustness to distributions shift

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

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

A Novel Decomposition Solution Approach for the Restoration Problem in Distribution Networks

Fair Colorful k-Center Clustering

Variational Inference with Mixture Model Approximation for Applications in Robotics

Multimodal water age distributions and the challenge of complex hydrological landscapes

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration

Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations

Distributionally Robust Mechanism Design

Redundant features can hurt robustness to distributions shift