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2 code implementations • 9 Sep 2022 • Insu Han, Amir Zandieh, Jaehoon Lee, Roman Novak, Lechao Xiao, Amin Karbasi

Moreover, most prior works on neural kernels have focused on the ReLU activation, mainly due to its popularity but also due to the difficulty of computing such kernels for general activations.

1 code implementation • 1 Jul 2022 • Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi

In this work, we develop a scalable MCMC sampling algorithm for $k$-NDPPs with low-rank kernels, thus enabling runtime that is sublinear in $n$.

no code implementations • 25 Feb 2022 • Amir Zandieh, Insu Han, Haim Avron

We propose an algorithm for robust recovery of the spherical harmonic expansion of functions defined on the d-dimensional unit sphere $\mathbb{S}^{d-1}$ using a near-optimal number of function evaluations.

no code implementations • 7 Feb 2022 • Insu Han, Amir Zandieh, Haim Avron

Our proposed GZK family, generalizes the zonal kernels (i. e., dot-product kernels on the unit sphere) by introducing radial factors in their Gegenbauer series expansion, and includes a wide range of ubiquitous kernel functions such as the entirety of dot-product kernels as well as the Gaussian and the recently introduced Neural Tangent kernels.

1 code implementation • ICLR 2022 • Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi

However, existing work leaves open the question of scalable NDPP sampling.

1 code implementation • NeurIPS 2021 • Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

To accelerate learning with NTK, we design a near input-sparsity time approximation algorithm for NTK, by sketching the polynomial expansions of arc-cosine kernels: our sketch for the convolutional counterpart of NTK (CNTK) can transform any image using a linear runtime in the number of pixels.

1 code implementation • 3 Apr 2021 • Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

We combine random features of the arc-cosine kernels with a sketching-based algorithm which can run in linear with respect to both the number of data points and input dimension.

2 code implementations • ICLR 2021 • Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel

Determinantal point processes (DPPs) have attracted significant attention in machine learning for their ability to model subsets drawn from a large item collection.

1 code implementation • ICML 2020 • Insu Han, Haim Avron, Jinwoo Shin

This paper studies how to sketch element-wise functions of low-rank matrices.

1 code implementation • NeurIPS 2018 • Insu Han, Haim Avron, Jinwoo Shin

A large class of machine learning techniques requires the solution of optimization problems involving spectral functions of parametric matrices, e. g. log-determinant and nuclear norm.

1 code implementation • ICML 2017 • Insu Han, Prabhanjan Kambadur, KyoungSoo Park, Jinwoo Shin

Determinantal point processes (DPPs) are popular probabilistic models that arise in many machine learning tasks, where distributions of diverse sets are characterized by matrix determinants.

1 code implementation • 3 Jun 2016 • Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin

Computation of the trace of a matrix function plays an important role in many scientific computing applications, including applications in machine learning, computational physics (e. g., lattice quantum chromodynamics), network analysis and computational biology (e. g., protein folding), just to name a few application areas.

Data Structures and Algorithms

1 code implementation • 22 Mar 2015 • Insu Han, Dmitry Malioutov, Jinwoo Shin

Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partition functions of discrete graphical models, minimum-volume ellipsoids, metric learning and kernel learning.

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