Publications associées à Locally Differentially-Private Randomized Response for Discrete Distribution Learning

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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

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Graph Chatbot

Chattez avec Graph Search

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Uncertain Sampling with Certain Priors

Some first results on the consistency of spatial regression with partial differential equation regularization

Robust Generalization via f−Mutual Information

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

From Data to Decisions: Distributionally Robust Optimization is Optimal

Uncertain Sampling with Certain Priors

Lower-bounds on the Bayesian Risk in Estimation Procedures via f–Divergences

DATED: GUIDELINES FOR CREATING SYNTHETIC DATASETS FOR ENGINEERING DESIGN APPLICATIONS

Generalization Error Bounds Via Rényi-, f-Divergences and Maximal Leakage

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance

Some first results on the consistency of spatial regression with partial differential equation regularization

Robust Generalization via f−Mutual Information

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

From Data to Decisions: Distributionally Robust Optimization is Optimal