Parallel unsymmetric-pattern multifrontal sparse LU with column preordering. - Haim Avron; Gil Shklarski; Sivan Toledo (ACM Trans. Math. Softw., 2008)

PFunc: modern task parallelism for modern high performance computing. - Prabhanjan Kambadur; Anshul Gupta; Amol Ghoting; Haim Avron; Andrew Lumsdaine (SC, 2009)

On Element SDD Approximability - Haim Avron; Gil Shklarski; Sivan Toledo (CoRR, 2009)

Combinatorial Preconditioners for Scalar Elliptic Finite-Element Problems. - Haim Avron; Doron Chen; Gil Shklarski; Sivan Toledo (SIAM J. Matrix Anal. Appl., 2009)

Using Perturbed QR Factorizations to Solve Linear Least-Squares Problems. - Haim Avron; Esmond Ng; Sivan Toledo (SIAM J. Matrix Anal. Appl., 2009)

Blendenpik: Supercharging LAPACK's Least-Squares Solver. - Haim Avron; Petar Maymounkov; Sivan Toledo (SIAM J. Sci. Comput., 2010)

l1-Sparse reconstruction of sharp point set surfaces. - Haim Avron; Andrei Sharf; Chen Greif; Daniel Cohen-Or (ACM Trans. Graph., 2010)

Advanced algorithmic techniques in numerical linear algebra: hybridization and randomization - Haim Avron (2010)

Effective Stiffness: Generalizing Effective Resistance Sampling to Finite Element Matrices - Haim Avron; Sivan Toledo (CoRR, 2011)

Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix. - Haim Avron; Sivan Toledo (J. ACM, 2011)

Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. - Haim Avron; Satyen Kale; Shiva Prasad Kasiviswanathan; Vikas Sindhwani (ICML, 2012)

Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers. - Haim Avron; Anshul Gupta (SC, 2012)

Sketching Structured Matrices for Faster Nonlinear Regression. - Haim Avron; Vikas Sindhwani; David P. Woodruff (NIPS, 2013)

Reliable Iterative Condition-Number Estimation - Haim Avron; Alex Druinsky; Sivan Toledo (CoRR, 2013)

A Randomized Asynchronous Linear Solver with Provable Convergence Rate - Haim Avron; Alex Druinsky; Anshul Gupta (CoRR, 2013)

Solving Hermitian positive definite systems using indefinite incomplete factorizations. - Haim Avron; Anshul Gupta; Sivan Toledo (J. Comput. Appl. Math., 2013)

Faster Subset Selection for Matrices and Applications. - Haim Avron; Christos Boutsidis (SIAM J. Matrix Anal. Appl., 2013)

Random Laplace Feature Maps for Semigroup Kernels on Histograms. - Jiyan Yang; Vikas Sindhwani; Quanfu Fan; Haim Avron; Michael W. Mahoney (CVPR, 2014)

Kernel methods match Deep Neural Networks on TIMIT. - Po-Sen Huang; Haim Avron; Tara N. Sainath; Vikas Sindhwani; Bhuvana Ramabhadran (ICASSP, 2014)

Subspace Embeddings for the Polynomial Kernel. - Haim Avron; Huy L. Nguyen; David P. Woodruff (NIPS, 2014)

Efficient Dimensionality Reduction for Canonical Correlation Analysis. - Haim Avron; Christos Boutsidis; Sivan Toledo; Anastasios Zouzias (SIAM J. Sci. Comput., 2014)

Community Detection Using Time-Dependent Personalized PageRank. - Haim Avron; Lior Horesh (ICML, 2015)

A scalable randomized least squares solver for dense overdetermined systems. - Chander Iyer; Haim Avron; Georgios Kollias; Yves Ineichen; Christopher D. Carothers; Petros Drineas (ScalA@SC, 2015)

Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. - Haim Avron; Alex Druinsky; Anshul Gupta (J. ACM, 2015)

Sharper Bounds for Regression and Low-Rank Approximation with Regularization. - Haim Avron; Kenneth L. Clarkson; David P. Woodruff (CoRR, 2016)

Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. - Insu Han; Dmitry Malioutov; Haim Avron; Jinwoo Shin (CoRR, 2016)

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. - Haim Avron; Vikas Sindhwani; Jiyan Yang; Michael W. Mahoney (J. Mach. Learn. Res., 2016)

High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization. - Haim Avron; Vikas Sindhwani (Technometrics, 2016)

Sharper Bounds for Regularized Data Fitting. - Haim Avron; Kenneth L. Clarkson; David P. Woodruff (APPROX-RANDOM, 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 (ICML, 2017)

Hierarchically Compositional Kernels for Scalable Nonparametric Learning. - Jie Chen 007; Haim Avron; Vikas Sindhwani (J. Mach. Learn. Res., 2017)

Faster Kernel Ridge Regression Using Sketching and Preconditioning. - Haim Avron; Kenneth L. Clarkson; David P. Woodruff (SIAM J. Matrix Anal. Appl., 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)

Stochastic Chebyshev Gradient Descent for Spectral Optimization. - Insu Han; Haim Avron; Jinwoo Shin (NeurIPS, 2018)

Optimizing Spectral Sums using Randomized Chebyshev Expansions. - Insu Han; Haim Avron; Jinwoo Shin (CoRR, 2018)

Stable Tensor Neural Networks for Rapid Deep Learning. - Elizabeth Newman; Lior Horesh; Haim Avron; Misha E. Kilmer (CoRR, 2018)

Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. - Gal Shulkind; Lior Horesh; Haim Avron (SIAM/ASA J. Uncertain. Quantification, 2018)

A universal sampling method for reconstructing signals with simple Fourier transforms. - Haim Avron; Michael Kapralov; Cameron Musco; Christopher Musco; Ameya Velingker; Amir Zandieh (STOC, 2019)

Randomized Riemannian Preconditioning for Quadratically Constrained Problems. - Boris Shustin; Haim Avron (CoRR, 2019)

Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. - Osman Asif Malik; Shashanka Ubaru; Lior Horesh; Misha E. Kilmer; Haim Avron (CoRR, 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)

Spectral condition-number estimation of large sparse matrices. - Haim Avron; Alex Druinsky; Sivan Toledo (Numer. Linear Algebra Appl., 2019)

Sketching for Principal Component Regression. - Liron Mor-Yosef; Haim Avron (SIAM J. Matrix Anal. Appl., 2019)

Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. - Insu Han; Haim Avron; Jinwoo Shin (ICML, 2020)

Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. - Agniva Chowdhury; Palma London; Haim Avron; Petros Drineas (NeurIPS, 2020)

Tensor-Tensor Products for Optimal Representation and Compression. - Misha E. Kilmer; Lior Horesh; Haim Avron; Elizabeth Newman (CoRR, 2020)

Speeding up Linear Programming using Randomized Linear Algebra. - Agniva Chowdhury; Palma London; Haim Avron; Petros Drineas (CoRR, 2020)

Solving Trust Region Subproblems Using Riemannian Optimization. - Uria Mor; Haim Avron (CoRR, 2020)

Quasi Monte Carlo Time-Frequency Analysis. - Ron Levie; Haim Avron; Gitta Kutyniok (CoRR, 2020)

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 (HPEC, 2021)

Scaling Neural Tangent Kernels via Sketching and Random Features. - Amir Zandieh; Insu Han; Haim Avron; Neta Shoham; Chaewon Kim; Jinwoo Shin (NeurIPS, 2021)

Dynamic Graph Convolutional Networks Using the Tensor M-Product. - Osman Asif Malik; Shashanka Ubaru; Lior Horesh; Misha E. Kilmer; Haim Avron (SDM, 2021)

Random Features for the Neural Tangent Kernel. - Insu Han; Haim Avron; Neta Shoham; Chaewon Kim; Jinwoo Shin (CoRR, 2021)

Faster Randomized Methods for Orthogonality Constrained Problems. - Boris Shustin; Haim Avron (CoRR, 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)

On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. - Gregory Dexter; Agniva Chowdhury; Haim Avron; Petros Drineas (ICML, 2022)

Random Gegenbauer Features for Scalable Kernel Methods. - Insu Han; Amir Zandieh; Haim Avron (ICML, 2022)

Randomized continuous frames in time-frequency analysis. - Ron Levie; Haim Avron (Adv. Comput. Math., 2022)

Low-Rank Updates of Matrix Square Roots. - Shany Shumeli; Petros Drineas; Haim Avron (CoRR, 2022)

PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty. - Paz Fink Shustin; Shashanka Ubaru; Vasileios Kalantzis; Lior Horesh; Haim Avron (CoRR, 2022)

Manifold Free Riemannian Optimization. - Boris Shustin; Haim Avron; Barak Sober (CoRR, 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)

Gauss-Legendre Features for Gaussian Process Regression. - Paz Fink Shustin; Haim Avron (J. Mach. Learn. Res., 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)

Semi-Infinite Linear Regression and Its Applications. - Paz Fink Shustin; Haim Avron (SIAM J. Matrix Anal. Appl., 2022)

Near Optimal Reconstruction of Spherical Harmonic Expansions. - Amir Zandieh; Insu Han; Haim Avron (NeurIPS, 2023)

Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation. - Raphael A. Meyer; Haim Avron (CoRR, 2023)

Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold. - Boris Shustin; Haim Avron (J. Comput. Appl. Math., 2023)

Solving trust region subproblems using Riemannian optimization. - Uria Mor; Boris Shustin; Haim Avron (Numerische Mathematik, 2023)

Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning. - Neta Shoham; Haim Avron (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

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; et al. (CoRR, 2024)

On the Role of Initialization on the Implicit Bias in Deep Linear Networks. - Oria Gruber; Haim Avron (CoRR, 2024)

Stable tensor neural networks for efficient deep learning. - Elizabeth Newman; Lior Horesh; Haim Avron; Misha E. Kilmer (Frontiers Big Data, 2024)

Low-rank updates of matrix square roots. - Shany Shmueli; Petros Drineas; Haim Avron (Numer. Linear Algebra Appl., 2024)

Multivariate Trace Estimation Using Quantum State Space Linear Algebra. - Liron Mor-Yosef; Shashanka Ubaru; Lior Horesh; Haim Avron (SIAM J. Matrix Anal. Appl., 2025)

Higher Order Reduced Rank Regression. - Leia Greenberg; Haim Avron (CoRR, 2025)

Quasitubal Tensor Algebra Over Separable Hilbert Spaces. - Uria Mor; Haim Avron (CoRR, 2025)

Demystifying Tubal Tensor Algebra. - Haim Avron; Uria Mor (CoRR, 2025)

Flatness After All? - Neta Shoham; Liron Mor-Yosef; Haim Avron (CoRR, 2025)