Haim Avron

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 65

Top Topics

Kernel Ridge Regression

Interior Point Methods

Top Venues

J. Mach. Learn. Res.

Publications

Oria Gruber, Haim Avron
On the Role of Initialization on the Implicit Bias in Deep Linear Networks. CoRR (2024)
Shany Shmueli, Petros Drineas, Haim Avron
Low-rank updates of matrix square roots. Numer. Linear Algebra Appl. 31 (1) (2024)
Vassilis Kalantzis, Mark S. Squillante, Shashanka Ubaru, Tayfun Gokmen, Chai Wah Wu, Anshul Gupta, Haim Avron, Tomasz Nowicki, Malte J. Rasch, Murat Onen, Vanessa López-Marrero, Effendi Leobandung, Yasuteru Kohda, Wilfried Haensch, Lior Horesh
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR (2024)
Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron
Multivariate trace estimation using quantum state space linear algebra. CoRR (2024)
Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer
Stable tensor neural networks for efficient deep learning. Frontiers Big Data 7 (2024)
Neta Shoham, Haim Avron
Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45 (10) (2023)
Raphael A. Meyer, Haim Avron
Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation. CoRR (2023)
Boris Shustin, Haim Avron
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold. J. Comput. Appl. Math. 423 (2023)
Amir Zandieh, Insu Han, Haim Avron
Near Optimal Reconstruction of Spherical Harmonic Expansions. NeurIPS (2023)
Uria Mor, Boris Shustin, Haim Avron
Solving trust region subproblems using Riemannian optimization. Numerische Mathematik 154 (1-2) (2023)
Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron
Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations. PLoS Comput. Biol. 18 (7) (2022)
Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. ICML (2022)
Boris Shustin, Haim Avron, Barak Sober
Manifold Free Riemannian Optimization. CoRR (2022)
Insu Han, Amir Zandieh, Haim Avron
Random Gegenbauer Features for Scalable Kernel Methods. ICML (2022)
Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty. CoRR (2022)
Paz Fink Shustin, Haim Avron
Semi-Infinite Linear Regression and Its Applications. SIAM J. Matrix Anal. Appl. 43 (1) (2022)
Shany Shumeli, Petros Drineas, Haim Avron
Low-Rank Updates of Matrix Square Roots. CoRR (2022)
Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs. J. Mach. Learn. Res. 23 (2022)
Amir Zandieh, Insu Han, Haim Avron
Near Optimal Reconstruction of Spherical Harmonic Expansions. CoRR (2022)
Paz Fink Shustin, Haim Avron
Gauss-Legendre Features for Gaussian Process Regression. J. Mach. Learn. Res. 23 (2022)
Ron Levie, Haim Avron
Randomized continuous frames in time-frequency analysis. Adv. Comput. Math. 48 (3) (2022)
Insu Han, Amir Zandieh, Haim Avron
Random Gegenbauer Features for Scalable Kernel Methods. CoRR (2022)
Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs. CoRR (2022)
Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron
Dynamic Graph Convolutional Networks Using the Tensor M-Product. SDM (2021)
Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
Scaling Neural Tangent Kernels via Sketching and Random Features. NeurIPS (2021)
Boris Shustin, Haim Avron
Faster Randomized Methods for Orthogonality Constrained Problems. CoRR (2021)
Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
Scaling Neural Tangent Kernels via Sketching and Random Features. CoRR (2021)
Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
Random Features for the Neural Tangent Kernel. CoRR (2021)
Paz Fink Shustin, Haim Avron
Semi-Infinite Linear Regression and Its Applications. CoRR (2021)
Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron
Solving sparse linear systems with approximate inverse preconditioners on analog devices. HPEC (2021)
Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization. CoRR (2021)
Paz Fink Shustin, Haim Avron
Gauss-Legendre Features for Gaussian Process Regression. CoRR (2021)
Misha E. Kilmer, Lior Horesh, Haim Avron, Elizabeth Newman
Tensor-Tensor Products for Optimal Representation and Compression. CoRR (2020)
Ron Levie, Haim Avron, Gitta Kutyniok
Quasi Monte Carlo Time-Frequency Analysis. CoRR (2020)
Neta Shoham, Haim Avron
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning. CoRR (2020)
Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. NeurIPS (2020)
Uria Mor, Haim Avron
Solving Trust Region Subproblems Using Riemannian Optimization. CoRR (2020)
Insu Han, Haim Avron, Jinwoo Shin
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. ICML (2020)
Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas
Speeding up Linear Programming using Randomized Linear Algebra. CoRR (2020)
Ron Levie, Haim Avron
Randomized Continuous Frames in Time-Frequency Analysis. CoRR (2020)
Liron Mor-Yosef, Haim Avron
Sketching for Principal Component Regression. SIAM J. Matrix Anal. Appl. 40 (2) (2019)
Haim Avron, Alex Druinsky, Sivan Toledo
Spectral condition-number estimation of large sparse matrices. Numer. Linear Algebra Appl. 26 (3) (2019)
Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. CoRR (2019)
Boris Shustin, Haim Avron
Randomized Riemannian Preconditioning for Quadratically Constrained Problems. CoRR (2019)
Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas
A randomized least squares solver for terabyte-sized dense overdetermined systems. J. Comput. Sci. 36 (2019)
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
A universal sampling method for reconstructing signals with simple Fourier transforms. STOC (2019)
Insu Han, Haim Avron, Jinwoo Shin
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. CoRR (2019)
Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer
Stable Tensor Neural Networks for Rapid Deep Learning. CoRR (2018)
Insu Han, Haim Avron, Jinwoo Shin
Stochastic Chebyshev Gradient Descent for Spectral Optimization. NeurIPS (2018)
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms. CoRR (2018)
Insu Han, Haim Avron, Jinwoo Shin
Optimizing Spectral Sums using Randomized Chebyshev Expansions. CoRR (2018)
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. CoRR (2018)
Gal Shulkind, Lior Horesh, Haim Avron
Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. SIAM/ASA J. Uncertain. Quantification 6 (2) (2018)
Liron Mor-Yosef, Haim Avron
Sketching for Principal Component Regression. CoRR (2018)
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. ICML (2017)
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Faster Kernel Ridge Regression Using Sketching and Preconditioning. SIAM J. Matrix Anal. Appl. 38 (4) (2017)
Jie Chen, Haim Avron, Vikas Sindhwani
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. J. Mach. Learn. Res. 18 (2017)
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Sharper Bounds for Regularized Data Fitting. APPROX-RANDOM (2017)
Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin
Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations. SIAM J. Sci. Comput. 39 (4) (2017)
Jie Chen, Haim Avron, Vikas Sindhwani
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. CoRR (2016)
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Faster Kernel Ridge Regression Using Sketching and Preconditioning. CoRR (2016)
Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. 17 (2016)
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Sharper Bounds for Regression and Low-Rank Approximation with Regularization. CoRR (2016)
Haim Avron, Vikas Sindhwani
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization. Technometrics 58 (3) (2016)
Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. CoRR (2016)
Haim Avron, Lior Horesh
Community Detection Using Time-Dependent Personalized PageRank. ICML (2015)
Haim Avron, Alex Druinsky, Anshul Gupta
Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. J. ACM 62 (6) (2015)
Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas
A scalable randomized least squares solver for dense overdetermined systems. ScalA@SC (2015)
Haim Avron, Huy L. Nguyen, David P. Woodruff
Subspace Embeddings for the Polynomial Kernel. NIPS (2014)
Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML (2014)
Haim Avron, Alex Druinsky, Anshul Gupta
Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. IPDPS (2014)
Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran
Kernel methods match Deep Neural Networks on TIMIT. ICASSP (2014)
Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
Efficient Dimensionality Reduction for Canonical Correlation Analysis. SIAM J. Sci. Comput. 36 (5) (2014)
Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR (2014)
Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney
Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR (2014)
Vikas Sindhwani, Haim Avron
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization. CoRR (2014)
Haim Avron, Alex Druinsky, Anshul Gupta
A Randomized Asynchronous Linear Solver with Provable Convergence Rate CoRR (2013)
Haim Avron, Christos Boutsidis
Faster Subset Selection for Matrices and Applications. SIAM J. Matrix Anal. Appl. 34 (4) (2013)
Haim Avron, Anshul Gupta, Sivan Toledo
Solving Hermitian positive definite systems using indefinite incomplete factorizations. J. Comput. Appl. Math. 243 (2013)
Haim Avron, Alex Druinsky, Sivan Toledo
Reliable Iterative Condition-Number Estimation CoRR (2013)
Haim Avron, Vikas Sindhwani, David P. Woodruff
Sketching Structured Matrices for Faster Nonlinear Regression. NIPS (2013)
Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
Efficient Dimensionality Reduction for Canonical Correlation Analysis. ICML (1) (2013)
Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
Efficient Dimensionality Reduction for Canonical Correlation Analysis CoRR (2012)
Haim Avron, Christos Boutsidis
Faster Subset Selection for Matrices and Applications CoRR (2012)
Haim Avron, Anshul Gupta
Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers. SC (2012)
Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML (2012)
Haim Avron, Sivan Toledo
Effective Stiffness: Generalizing Effective Resistance Sampling to Finite Element Matrices CoRR (2011)
Haim Avron, Sivan Toledo
Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix. J. ACM 58 (2) (2011)
Haim Avron, Petar Maymounkov, Sivan Toledo
Blendenpik: Supercharging LAPACK's Least-Squares Solver. SIAM J. Sci. Comput. 32 (3) (2010)
Haim Avron, Andrei Sharf, Chen Greif, Daniel Cohen-Or
-Sparse reconstruction of sharp point set surfaces. ACM Trans. Graph. 29 (5) (2010)
Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine
PFunc: modern task parallelism for modern high performance computing. SC (2009)
Haim Avron, Doron Chen, Gil Shklarski, Sivan Toledo
Combinatorial Preconditioners for Scalar Elliptic Finite-Element Problems. SIAM J. Matrix Anal. Appl. 31 (2) (2009)
Haim Avron, Gil Shklarski, Sivan Toledo
On Element SDD Approximability CoRR (2009)
Haim Avron, Esmond Ng, Sivan Toledo
Using Perturbed QR Factorizations to Solve Linear Least-Squares Problems. SIAM J. Matrix Anal. Appl. 31 (2) (2009)
Haim Avron, Gil Shklarski, Sivan Toledo
Parallel unsymmetric-pattern multifrontal sparse LU with column preordering. ACM Trans. Math. Softw. 34 (2) (2008)