Publications associées à Noyau (algèbre)

End-to-end kernel learning via generative random Fourier features

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

Midas: Systematic Kernel TOCTTOU Protection

Mathias Josef Payer, Atri Bhattacharyya, Uros Tesic

Double-fetch bugs are a plague across all major operating system kernels. They occur when data is fetched twice across the user/kernel trust boundary while allowing concurrent modification. Such bugs enable an attacker to illegally access memory, cause den ...

2022

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Variants of Homomorphism Polynomials Complete for Algebraic Complexity Classes

Aditya Vardhan Varre

We present polynomial families complete for the well-studied algebraic complexity classes VF, VBP, VP, and VNP. The polynomial families are based on the homomorphism polynomials studied in the recent works of Durand et al. (2014) and Mahajan et al. (2018). ...

ASSOC COMPUTING MACHINERY2021

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

Colin Neil Jones, Paul Scharnhorst, Emilio Maddalena

We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and epsilon-support vector regression. By assuming the ground-truth function belongs to th ...

PERGAMON-ELSEVIER SCIENCE LTD2021

NrOS: Effective Replication and Sharing in an Operating System

Sanidhya Kashyap, Ankit Bhardwaj

Writing a correct operating system kernel is notoriously hard. Kernel code requires manual memory management and type-unsafe code and must efficiently handle complex, asynchronous events. In addition, increasing CPU core counts further complicate kernel de ...

USENIX ASSOC2021

Oblivious Sketching of High-Degree Polynomial Kernels

Mikhail Kapralov, Amir Zandieh

Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor scalabili ...

ASSOC COMPUTING MACHINERY2020

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Mikhail Kapralov, Amir Zandieh, Navid Nouri

Random binning features, introduced in the seminal paper of Rahimi and Recht '07, are an efficient method for approximating a kernel matrix using locality sensitive hashing. Random binning features provide a very simple and efficient way to approximate the ...

ADDISON-WESLEY PUBL CO2020

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Noyau (algèbre)

End-to-end kernel learning via generative random Fourier features

Midas: Systematic Kernel TOCTTOU Protection

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Variants of Homomorphism Polynomials Complete for Algebraic Complexity Classes

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

NrOS: Effective Replication and Sharing in an Operating System

Multikernel Regression with Sparsity Constraint

Oblivious Sketching of High-Degree Polynomial Kernels

Computing Cyclic Isogenies between Principally Polarized Abelian Varieties over Finite Fields

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Graph Chatbot

Chattez avec Graph Search

NrOS: Effective Replication and Sharing in an Operating System

Midas: Systematic Kernel TOCTTOU Protection

End-to-end kernel learning via generative random Fourier features

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

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

Multikernel Regression with Sparsity Constraint

Oblivious Sketching of High-Degree Polynomial Kernels

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing