54 citations · 79 across the 14 of their papers we have counts for
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Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS
Neta Shoham, Haim Avron
Current methods for stochastic hyperparameter learning in Gaussian Processes (GPs) rely on approximations, such as computing biased stochastic gradients or using inducing points in…
Flatness After All?
Neta Shoham, Liron Mor-Yosef, Haim Avron
Recent literature generalization in deep learning has examined the relationship between the curvature of the loss function at minima and generalization, mainly in the context of ov…
Random Gegenbauer Features for Scalable Kernel Methods
Insu Han, Amir Zandieh, Haim Avron
We propose efficient random features for approximating a new and rich class of kernel functions that we refer to as Generalized Zonal Kernels (GZK). Our proposed GZK family, genera…
Random Features for the Neural Tangent Kernel
Insu Han, Haim Avron, Neta Shoham +2
The Neural Tangent Kernel (NTK) has discovered connections between deep neural networks and kernel methods with insights of optimization and generalization. Motivated by this, rece…
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning
Neta Shoham, Haim Avron
The impressive performance exhibited by modern machine learning models hinges on the ability to train such models on a very large amounts of labeled data. However, since access to…
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Insu Han, Haim Avron, Jinwoo Shin
This paper studies how to sketch element-wise functions of low-rank matrices. Formally, given low-rank matrix A = [Aij] and scalar non-linear function f, we aim for finding an appr…