Publications associées à Kernel Density Estimation through Density Constrained Near Neighbor Search

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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

Kernel Density Estimation through Density Constrained Near Neighbor Search

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Cluster-and-Conquer: When Randomness Meets Graph Locality

Oblivious Sketching of High-Degree Polynomial Kernels

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Robust discrete choice models with t-distributed kernel errors

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Testing Graph Clusterability: Algorithms and Lower Bounds

A Sampling Theory for Non-Decaying Signals

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization

Cluster-and-Conquer: When Randomness Meets Graph Locality

Oblivious Sketching of High-Degree Polynomial Kernels

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

A Sampling Theory for Non-Decaying Signals

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Testing Graph Clusterability: Algorithms and Lower Bounds

Scaling up Kernel Ridge Regression via Locality Sensitive Hashing

Robust discrete choice models with t-distributed kernel errors

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization