Publications related to Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

In this thesis, we propose to formally derive amplitude equations governing the weakly nonlinear evolution of non-normal dynamical systems, when they respond to harmonic or stochastic forcing, or to an initial condition. This approach reconciles the non-mo ...

EPFL2024

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

Fanghui Liu, Jie Yang

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

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

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

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

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

Proximity Results and Faster Algorithms for Integer Programming Using the Steinitz Lemma

Friedrich Eisenbrand

We consider integer programming problems in standard form max{c(T)x : Ax = b, x >= 0, x is an element of Z(n)} where A is an element of Z(mxn), b is an element of Z(m), and c is an element of Z(n). We show that such an integer program can be solved in time ...

ASSOC COMPUTING MACHINERY2020

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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Non-normal forms

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

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Midas: Systematic Kernel TOCTTOU Protection

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

Multikernel Regression with Sparsity Constraint

NrOS: Effective Replication and Sharing in an Operating System

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

Convex optimization in sums of Banach spaces

Proximity Results and Faster Algorithms for Integer Programming Using the Steinitz Lemma

Graph Chatbot

Chat with Graph Search

Non-normal forms

Convex optimization in sums of Banach spaces

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Proximity Results and Faster Algorithms for Integer Programming Using the Steinitz Lemma

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

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

Midas: Systematic Kernel TOCTTOU Protection

Multikernel Regression with Sparsity Constraint

NrOS: Effective Replication and Sharing in an Operating System

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