Publications associées à MATHICSE Technical Report : Density estimation in RKHS with application to Korobov spaces in high dimensions

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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

MATHICSE Technical Report : Density estimation in RKHS with application to Korobov spaces in high dimensions

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Chattez avec Graph Search

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Density Estimation In Rkhs With Application To Korobov Spaces In High Dimensions

Dynamically Orthogonal Approximation for Stochastic Differential Equations

Multikernel Regression with Sparsity Constraint

Deterministic error bounds for kernel-based learning techniques under bounded noise

MATHICSE Technical Report: Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Kernel Density Estimation through Density Constrained Near Neighbor Search

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Dynamically Orthogonal Approximation for Stochastic Differential Equations

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Kernel Density Estimation through Density Constrained Near Neighbor Search

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Density Estimation In Rkhs With Application To Korobov Spaces In High Dimensions

MATHICSE Technical Report: Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Multikernel Regression with Sparsity Constraint

Deterministic error bounds for kernel-based learning techniques under bounded noise

Existence of dynamical low rank approximations for random semi-linear evolutionary equations on the maximal interval