Haim Avron

According to our database, Haim Avron authored at least 75 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Low-rank updates of matrix square roots.

Shany Shmueli, Petros Drineas, Haim Avron

Numer. Linear Algebra Appl., January, 2024

Stable tensor neural networks for efficient deep learning.

Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer

Frontiers Big Data, 2024

Multivariate trace estimation using quantum state space linear algebra.

Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron

CoRR, 2024

On the Role of Initialization on the Implicit Bias in Deep Linear Networks.

Oria Gruber, Haim Avron

CoRR, 2024

Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation.

Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru, Tayfun Gokmen, Chai Wah Wu, Anshul Gupta, Haim Avron, Tomasz Nowicki, Malte J. Rasch, O. Murat Onen, Vanessa López-Marrero, Effendi Leobandung, Yasuteru Kohda, Wilfried Haensch, Lior Horesh

CoRR, 2024

2023

Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning.

Neta Shoham, Haim Avron

IEEE Trans. Pattern Anal. Mach. Intell., October, 2023

Solving trust region subproblems using Riemannian optimization.

Uria Mor, Boris Shustin, Haim Avron

Numerische Mathematik, June, 2023

Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold.

Boris Shustin, Haim Avron

J. Comput. Appl. Math., 2023

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

Raphael A. Meyer, Haim Avron

CoRR, 2023

Near Optimal Reconstruction of Spherical Harmonic Expansions.

Amir Zandieh, Insu Han, Haim Avron

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Semi-Infinite Linear Regression and Its Applications.

Paz Fink Shustin, Haim Avron

SIAM J. Matrix Anal. Appl., 2022

Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations.

Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron

PLoS Comput. Biol., 2022

Gauss-Legendre Features for Gaussian Process Regression.

Paz Fink Shustin, Haim Avron

J. Mach. Learn. Res., 2022

Faster Randomized Interior Point Methods for Tall/Wide Linear Programs.

Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas

J. Mach. Learn. Res., 2022

Manifold Free Riemannian Optimization.

Boris Shustin, Haim Avron, Barak Sober

CoRR, 2022

PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty.

Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron

CoRR, 2022

Low-Rank Updates of Matrix Square Roots.

Shany Shumeli, Petros Drineas, Haim Avron

CoRR, 2022

Randomized continuous frames in time-frequency analysis.

Ron Levie, Haim Avron

Adv. Comput. Math., 2022

Random Gegenbauer Features for Scalable Kernel Methods.

Insu Han, Amir Zandieh, Haim Avron

Proceedings of the International Conference on Machine Learning, 2022

On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming.

Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas

Proceedings of the International Conference on Machine Learning, 2022

2021

Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization.

Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron

CoRR, 2021

Faster Randomized Methods for Orthogonality Constrained Problems.

Boris Shustin, Haim Avron

CoRR, 2021

Random Features for the Neural Tangent Kernel.

Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

CoRR, 2021

Dynamic Graph Convolutional Networks Using the Tensor M-Product.

Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Scaling Neural Tangent Kernels via Sketching and Random Features.

Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Solving sparse linear systems with approximate inverse preconditioners on analog devices.

Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron

Proceedings of the 2021 IEEE High Performance Extreme Computing Conference, 2021

2020

Quasi Monte Carlo Time-Frequency Analysis.

Ron Levie, Haim Avron, Gitta Kutyniok

CoRR, 2020

Solving Trust Region Subproblems Using Riemannian Optimization.

Uria Mor, Haim Avron

CoRR, 2020

Speeding up Linear Programming using Randomized Linear Algebra.

Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas

CoRR, 2020

Tensor-Tensor Products for Optimal Representation and Compression.

Misha E. Kilmer, Lior Horesh, Haim Avron, Elizabeth Newman

CoRR, 2020

Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs.

Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Insu Han, Haim Avron, Jinwoo Shin

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Sketching for Principal Component Regression.

Liron Mor-Yosef, Haim Avron

SIAM J. Matrix Anal. Appl., 2019

Spectral condition-number estimation of large sparse matrices.

Haim Avron, Alex Druinsky, Sivan Toledo

Numer. Linear Algebra Appl., 2019

A randomized least squares solver for terabyte-sized dense overdetermined systems.

Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas

J. Comput. Sci., 2019

Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs.

Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron

CoRR, 2019

Randomized Riemannian Preconditioning for Quadratically Constrained Problems.

Boris Shustin, Haim Avron

CoRR, 2019

A universal sampling method for reconstructing signals with simple Fourier transforms.

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 2019

2018

Experimental Design for Nonparametric Correction of Misspecified Dynamical Models.

Gal Shulkind, Lior Horesh, Haim Avron

SIAM/ASA J. Uncertain. Quantification, 2018

Stable Tensor Neural Networks for Rapid Deep Learning.

Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer

CoRR, 2018

Optimizing Spectral Sums using Randomized Chebyshev Expansions.

Insu Han, Haim Avron, Jinwoo Shin

CoRR, 2018

Stochastic Chebyshev Gradient Descent for Spectral Optimization.

Insu Han, Haim Avron, Jinwoo Shin

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations.

Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin

SIAM J. Sci. Comput., 2017

Faster Kernel Ridge Regression Using Sketching and Preconditioning.

Haim Avron, Kenneth L. Clarkson, David P. Woodruff

SIAM J. Matrix Anal. Appl., 2017

Hierarchically Compositional Kernels for Scalable Nonparametric Learning.

Jie Chen, Haim Avron, Vikas Sindhwani

J. Mach. Learn. Res., 2017

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

Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh

Proceedings of the 34th International Conference on Machine Learning, 2017

Sharper Bounds for Regularized Data Fitting.

Haim Avron, Kenneth L. Clarkson, David P. Woodruff

Proceedings of the Approximation, 2017

2016

High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization.

Haim Avron, Vikas Sindhwani

Technometrics, 2016

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels.

Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney

J. Mach. Learn. Res., 2016

Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations.

Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin

CoRR, 2016

Sharper Bounds for Regression and Low-Rank Approximation with Regularization.

Haim Avron, Kenneth L. Clarkson, David P. Woodruff

CoRR, 2016

2015

Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization.

Haim Avron, Alex Druinsky, Anshul Gupta

J. ACM, 2015

A scalable randomized least squares solver for dense overdetermined systems.

Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas

Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems, 2015

Community Detection Using Time-Dependent Personalized PageRank.

Haim Avron, Lior Horesh

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Efficient Dimensionality Reduction for Canonical Correlation Analysis.

Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias

SIAM J. Sci. Comput., 2014

Subspace Embeddings for the Polynomial Kernel.

Haim Avron, Huy L. Nguyen, David P. Woodruff

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Kernel methods match Deep Neural Networks on TIMIT.

Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran

Proceedings of the IEEE International Conference on Acoustics, 2014

Random Laplace Feature Maps for Semigroup Kernels on Histograms.

Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney

Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

2013

Faster Subset Selection for Matrices and Applications.

Haim Avron, Christos Boutsidis

SIAM J. Matrix Anal. Appl., 2013

Solving Hermitian positive definite systems using indefinite incomplete factorizations.

Haim Avron, Anshul Gupta, Sivan Toledo

J. Comput. Appl. Math., 2013

A Randomized Asynchronous Linear Solver with Provable Convergence Rate

Haim Avron, Alex Druinsky, Anshul Gupta

CoRR, 2013

Reliable Iterative Condition-Number Estimation

Haim Avron, Alex Druinsky, Sivan Toledo

CoRR, 2013

Sketching Structured Matrices for Faster Nonlinear Regression.

Haim Avron, Vikas Sindhwani, David P. Woodruff

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers.

Haim Avron, Anshul Gupta

Proceedings of the SC Conference on High Performance Computing Networking, 2012

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization.

Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix.

Haim Avron, Sivan Toledo

J. ACM, 2011

Effective Stiffness: Generalizing Effective Resistance Sampling to Finite Element Matrices

Haim Avron, Sivan Toledo

CoRR, 2011

2010

Advanced algorithmic techniques in numerical linear algebra: hybridization and randomization

Haim Avron

PhD thesis, 2010

<i>l</i>₁-Sparse reconstruction of sharp point set surfaces.

Haim Avron, Andrei Sharf, Chen Greif, Daniel Cohen-Or

ACM Trans. Graph., 2010

Blendenpik: Supercharging LAPACK's Least-Squares Solver.

Haim Avron, Petar Maymounkov, Sivan Toledo

SIAM J. Sci. Comput., 2010

2009

Using Perturbed QR Factorizations to Solve Linear Least-Squares Problems.

Haim Avron, Esmond Ng, Sivan Toledo

SIAM J. Matrix Anal. Appl., 2009

Combinatorial Preconditioners for Scalar Elliptic Finite-Element Problems.

Haim Avron, Doron Chen, Gil Shklarski, Sivan Toledo

SIAM J. Matrix Anal. Appl., 2009

On Element SDD Approximability

Haim Avron, Gil Shklarski, Sivan Toledo

CoRR, 2009

PFunc: modern task parallelism for modern high performance computing.

Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine

Proceedings of the ACM/IEEE Conference on High Performance Computing, 2009

2008

Parallel unsymmetric-pattern multifrontal sparse LU with column preordering.

Haim Avron, Gil Shklarski, Sivan Toledo

ACM Trans. Math. Softw., 2008