b'Haim Avron'

research

∙ 09/10/2023

Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation

We study the problem of estimating the trace of a matrix 𝐀 that can only...

0 Raphael A. Meyer, et al. ∙

research

∙ 09/19/2022

Faster Randomized Interior Point Methods for Tall/Wide Linear Programs

Linear programming (LP) is an extremely useful tool which has been succe...

0 Agniva Chowdhury, et al. ∙

research

∙ 09/07/2022

Manifold Free Riemannian Optimization

Riemannian optimization is a principled framework for solving optimizati...

0 Boris Shustin, et al. ∙

research

∙ 02/25/2022

Near Optimal Reconstruction of Spherical Harmonic Expansions

We propose an algorithm for robust recovery of the spherical harmonic ex...

0 Amir Zandieh, et al. ∙

research

∙ 02/10/2022

PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty

Learning data representations under uncertainty is an important task tha...

0 Paz Fink Shustin, et al. ∙

research

∙ 02/07/2022

Random Gegenbauer Features for Scalable Kernel Methods

We propose efficient random features for approximating a new and rich cl...

0 Insu Han, et al. ∙

research

∙ 01/31/2022

Low-Rank Updates of Matrix Square Roots

Models in which the covariance matrix has the structure of a sparse matr...

0 Shany Shumeli, et al. ∙

research

∙ 11/28/2021

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization

Precision medicine is a clinical approach for disease prevention, detect...

0 Uria Mor, et al. ∙

research

∙ 06/22/2021

Faster Randomized Methods for Orthogonality Constrained Problems

Recent literature has advocated the use of randomized methods for accele...

0 Boris Shustin, et al. ∙

research

∙ 06/15/2021

Scaling Neural Tangent Kernels via Sketching and Random Features

The Neural Tangent Kernel (NTK) characterizes the behavior of infinitely...

7 Amir Zandieh, et al. ∙

research

∙ 04/12/2021

Semi-Infinite Linear Regression and Its Applications

Finite linear least squares is one of the core problems of numerical lin...

0 Paz Fink Shustin, et al. ∙

research

∙ 04/03/2021

Random Features for the Neural Tangent Kernel

The Neural Tangent Kernel (NTK) has discovered connections between deep ...

47 Insu Han, et al. ∙

research

∙ 01/04/2021

Gauss-Legendre Features for Gaussian Process Regression

Gaussian processes provide a powerful probabilistic kernel learning fram...

0 Paz Fink Shustin, et al. ∙

research

∙ 11/03/2020

Quasi Monte Carlo Time-Frequency Analysis

We study signal processing tasks in which the signal is mapped via some ...

0 Ron Levie, et al. ∙

research

∙ 10/15/2020

Solving Trust Region Subproblems Using Riemannian Optimization

The Trust Region Subproblem is a fundamental optimization problem that t...

0 Uria Mor, et al. ∙

research

∙ 09/27/2020

Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning

The impressive performance exhibited by modern machine learning models h...

0 Neta Shoham, et al. ∙

research

∙ 09/20/2020

Randomized Continuous Frames in Time-Frequency Analysis

Recently, a Monte Carlo approach was proposed for speeding up signal pro...

0 Ron Levie, et al. ∙

research

∙ 03/18/2020

Speeding up Linear Programming using Randomized Linear Algebra

Linear programming (LP) is an extremely useful tool and has been success...

0 Agniva Chowdhury, et al. ∙

research

∙ 12/31/2019

Tensor-Tensor Products for Optimal Representation and Compression

In this era of big data, data analytics and machine learning, it is impe...

0 Misha Kilmer, et al. ∙

research

∙ 10/16/2019

Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs

Many irregular domains such as social networks, financial transactions, ...

0 Osman Asif Malik, et al. ∙

research

∙ 05/28/2019

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix

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

8 Insu Han, et al. ∙

research

∙ 02/05/2019

Randomized Riemannian Preconditioning for Quadratically Constrained Problems

Optimization problem with quadratic equality constraints are prevalent i...

0 Boris Shustin, et al. ∙

research

∙ 12/20/2018

A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms

Reconstructing continuous signals from a small number of discrete sample...

0 Haim Avron, et al. ∙

research

∙ 11/15/2018

Stable Tensor Neural Networks for Rapid Deep Learning

We propose a tensor neural network (t-NN) framework that offers an excit...

0 Elizabeth Newman, et al. ∙

research

∙ 04/26/2018

Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees

Random Fourier features is one of the most popular techniques for scalin...

0 Haim Avron, et al. ∙

research

∙ 03/07/2018

Sketching for Principal Component Regression

Principal component regression (PCR) is a useful method for regularizing...

0 Liron Mor-Yosef, et al. ∙

research

∙ 02/18/2018

Optimizing Spectral Sums using Randomized Chebyshev Expansions

The trace of matrix functions, often called spectral sums, e.g., rank, l...

0 Insu Han, et al. ∙

research

∙ 11/12/2017

Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid First-Principles Data-Driven Modeling

Mathematical models are used extensively for diverse tasks including ana...

0 Remi R. Lam, et al. ∙

research

∙ 05/02/2017

Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models

We consider a class of misspecified dynamical models where the governing...

0 Gal Shulkind, et al. ∙

research

∙ 08/02/2016

Hierarchically Compositional Kernels for Scalable Nonparametric Learning

We propose a novel class of kernels to alleviate the high computational ...

0 Jie Chen, et al. ∙

research

∙ 12/29/2014

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

We consider the problem of improving the efficiency of randomized Fourie...

0 Haim Avron, et al. ∙

research

∙ 06/27/2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization

We describe novel subgradient methods for a broad class of matrix optimi...

0 Haim Avron, et al. ∙

Haim Avron

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