Publications associées à End-to-end kernel learning via generative random Fourier features

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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

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

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

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

The Linux Scheduler: A Decade of Wasted Cores

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

USBFuzz: A Framework for Fuzzing USB Drivers by Device Emulation

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Some Developments of the Cooray–Rubinstein Formula in the Time Domain

A New Formulation of the Cooray-Rubinstein Expression in Time Domain

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

The Linux Scheduler: A Decade of Wasted Cores

Advancing the State of Network Switch ASIC Offloading in the Linux Kernel

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

i-DPs CGRA: An Interleaved-Datapaths Reconfigurable Accelerator for Embedded Bio-signal Processing