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Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic

In this paper, we consider the problem of sequentially optimizing a black-box function

f

based on noisy samples and bandit feedback. We assume that

f

is smooth in the sense of having a bounded norm in some reproducing kernel Hilbert space (RKHS), yield ...

2017

Norme d'opérateur

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Functional Decomposition Using Majority

Functional Decomposition Using Majority

Lorentz Force from a Current-Carrying Wire on a Charge in Motion under the Assumption of Neutrality in the Symmetrical Frame of Reference

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Effective models for the multidimensional wave equation in heterogeneous media over long time and numerical homogenization

Dissipative Boundary Conditions for Nonlinear 1-D Hyperbolic Systems: Sharp Conditions Through an Approach via Time-Delay Systems

Stochastic Spectral Descent for Restricted Boltzmann Machines.

Homogenization of a multiscale viscoelastic model with nonlocal damping, application to the human lungs

Approximate Matrix Multiplication with Application to Linear Embeddings

Lorentz Force from a Current-Carrying Wire on a Charge in Motion under the Assumption of Neutrality in the Symmetrical Frame of Reference

Stochastic Spectral Descent for Restricted Boltzmann Machines.

Dissipative Boundary Conditions for Nonlinear 1-D Hyperbolic Systems: Sharp Conditions Through an Approach via Time-Delay Systems

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

General Proximal Gradient Method: A Case for Non-Euclidean Norms

Homogenization of a multiscale viscoelastic model with nonlocal damping, application to the human lungs

Functional Decomposition Using Majority

Functional Decomposition Using Majority

Effective models for the multidimensional wave equation in heterogeneous media over long time and numerical homogenization

Approximate Matrix Multiplication with Application to Linear Embeddings