Haim Avron - RankMe

104 papers A* 13A 4Misc 1Journal 79Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation. SIAM J. Matrix Anal. Appl. Raphael A. Meyer, Haim Avron
2025	J	`jnl`	Demystifying Tubal Tensor Algebra. CoRR Haim Avron, Uria Mor
2025	J	`jnl`	Flatness After All? CoRR Neta Shoham, Liron Mor-Yosef, Haim Avron
2025	J	`jnl`	Higher Order Reduced Rank Regression. CoRR Leia Greenberg, Haim Avron
2025	J	`jnl`	Multivariate Trace Estimation Using Quantum State Space Linear Algebra. SIAM J. Matrix Anal. Appl. Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron
2025	J	`jnl`	On Encoding Matrices using Quantum Circuits. CoRR Liron Mor-Yosef, Haim Avron
2025	J	`jnl`	Quasitubal Tensor Algebra Over Separable Hilbert Spaces. CoRR Uria Mor, Haim Avron
2025	J	`jnl`	Unbiased Stochastic Optimization for Gaussian Processes on Finite Dimensional RKHS. CoRR Neta Shoham, Haim Avron
2024	J	`jnl`	Faster Randomized Methods for Orthogonality Constrained Problems. J. Mach. Learn. Res. Boris Shustin, Haim Avron
2024	J	`jnl`	Low-rank updates of matrix square roots. Numer. Linear Algebra Appl. Shany Shmueli, Petros Drineas, Haim Avron
2024	J	`jnl`	Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR 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
2024	J	`jnl`	Multivariate trace estimation using quantum state space linear algebra. CoRR Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron
2024	J	`jnl`	On the Role of Initialization on the Implicit Bias in Deep Linear Networks. CoRR Oria Gruber, Haim Avron
2024	J	`jnl`	Stable tensor neural networks for efficient deep learning. Frontiers Big Data Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer
2023	J	`jnl`	Experimental Design for Overparameterized Learning With Application to Single Shot Deep Active Learning. IEEE Trans. Pattern Anal. Mach. Intell. Neta Shoham, Haim Avron
2023	J	`jnl`	Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation. CoRR Raphael A. Meyer, Haim Avron
2023	A*	`conf`	Near Optimal Reconstruction of Spherical Harmonic Expansions. NeurIPS Amir Zandieh, Insu Han, Haim Avron
2023	J	`jnl`	Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold. J. Comput. Appl. Math. Boris Shustin, Haim Avron
2023	J	`jnl`	Solving trust region subproblems using Riemannian optimization. Numerische Mathematik Uria Mor, Boris Shustin, Haim Avron
2022	J	`jnl`	Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations. PLoS Comput. Biol. Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron
2022	J	`jnl`	Faster Randomized Interior Point Methods for Tall/Wide Linear Programs. CoRR Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas
2022	J	`jnl`	Faster Randomized Interior Point Methods for Tall/Wide Linear Programs. J. Mach. Learn. Res. Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas
2022	J	`jnl`	Gauss-Legendre Features for Gaussian Process Regression. J. Mach. Learn. Res. Paz Fink Shustin, Haim Avron
2022	J	`jnl`	Low-Rank Updates of Matrix Square Roots. CoRR Shany Shumeli, Petros Drineas, Haim Avron
2022	J	`jnl`	Manifold Free Riemannian Optimization. CoRR Boris Shustin, Haim Avron, Barak Sober
2022	J	`jnl`	Near Optimal Reconstruction of Spherical Harmonic Expansions. CoRR Amir Zandieh, Insu Han, Haim Avron
2022	A*	`conf`	On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. ICML Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas
2022	J	`jnl`	PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty. CoRR Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron
2022	A*	`conf`	Random Gegenbauer Features for Scalable Kernel Methods. ICML Insu Han, Amir Zandieh, Haim Avron
2022	J	`jnl`	Random Gegenbauer Features for Scalable Kernel Methods. CoRR Insu Han, Amir Zandieh, Haim Avron
2022	J	`jnl`	Randomized continuous frames in time-frequency analysis. Adv. Comput. Math. Ron Levie, Haim Avron
2022	J	`jnl`	Semi-Infinite Linear Regression and Its Applications. SIAM J. Matrix Anal. Appl. Paz Fink Shustin, Haim Avron
2021	J	`jnl`	Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization. CoRR Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron
2021	A	`conf`	Dynamic Graph Convolutional Networks Using the Tensor M-Product. SDM Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron
2021	J	`jnl`	Faster Randomized Methods for Orthogonality Constrained Problems. CoRR Boris Shustin, Haim Avron
2021	J	`jnl`	Gauss-Legendre Features for Gaussian Process Regression. CoRR Paz Fink Shustin, Haim Avron
2021	J	`jnl`	Random Features for the Neural Tangent Kernel. CoRR Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
2021	A*	`conf`	Scaling Neural Tangent Kernels via Sketching and Random Features. NeurIPS Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
2021	J	`jnl`	Scaling Neural Tangent Kernels via Sketching and Random Features. CoRR Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin
2021	J	`jnl`	Semi-Infinite Linear Regression and Its Applications. CoRR Paz Fink Shustin, Haim Avron
2021	—	`conf`	Solving sparse linear systems with approximate inverse preconditioners on analog devices. HPEC Vasileios Kalantzis, Anshul Gupta, Lior Horesh, Tomasz Nowicki, Mark S. Squillante, Chai Wah Wu, Tayfun Gokmen, Haim Avron
2020	J	`jnl`	Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning. CoRR Neta Shoham, Haim Avron
2020	A*	`conf`	Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. NeurIPS Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas
2020	A*	`conf`	Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. ICML Insu Han, Haim Avron, Jinwoo Shin
2020	J	`jnl`	Quasi Monte Carlo Time-Frequency Analysis. CoRR Ron Levie, Haim Avron, Gitta Kutyniok
2020	J	`jnl`	Randomized Continuous Frames in Time-Frequency Analysis. CoRR Ron Levie, Haim Avron
2020	J	`jnl`	Solving Trust Region Subproblems Using Riemannian Optimization. CoRR Uria Mor, Haim Avron
2020	J	`jnl`	Speeding up Linear Programming using Randomized Linear Algebra. CoRR Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas
2020	J	`jnl`	Tensor-Tensor Products for Optimal Representation and Compression. CoRR Misha E. Kilmer, Lior Horesh, Haim Avron, Elizabeth Newman
2019	J	`jnl`	A randomized least squares solver for terabyte-sized dense overdetermined systems. J. Comput. Sci. Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas
2019	A*	`conf`	A universal sampling method for reconstructing signals with simple Fourier transforms. STOC Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
2019	J	`jnl`	Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. CoRR Insu Han, Haim Avron, Jinwoo Shin
2019	J	`jnl`	Randomized Riemannian Preconditioning for Quadratically Constrained Problems. CoRR Boris Shustin, Haim Avron
2019	J	`jnl`	Sketching for Principal Component Regression. SIAM J. Matrix Anal. Appl. Liron Mor-Yosef, Haim Avron
2019	J	`jnl`	Spectral condition-number estimation of large sparse matrices. Numer. Linear Algebra Appl. Haim Avron, Alex Druinsky, Sivan Toledo
2019	J	`jnl`	Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. CoRR Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron
2018	J	`jnl`	A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms. CoRR Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
2018	J	`jnl`	Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. SIAM/ASA J. Uncertain. Quantification Gal Shulkind, Lior Horesh, Haim Avron
2018	J	`jnl`	Optimizing Spectral Sums using Randomized Chebyshev Expansions. CoRR Insu Han, Haim Avron, Jinwoo Shin
2018	J	`jnl`	Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. CoRR Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
2018	J	`jnl`	Sketching for Principal Component Regression. CoRR Liron Mor-Yosef, Haim Avron
2018	J	`jnl`	Stable Tensor Neural Networks for Rapid Deep Learning. CoRR Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer
2018	A*	`conf`	Stochastic Chebyshev Gradient Descent for Spectral Optimization. NeurIPS Insu Han, Haim Avron, Jinwoo Shin
2017	J	`jnl`	Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations. SIAM J. Sci. Comput. Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin
2017	J	`jnl`	Faster Kernel Ridge Regression Using Sketching and Preconditioning. SIAM J. Matrix Anal. Appl. Haim Avron, Kenneth L. Clarkson, David P. Woodruff
2017	J	`jnl`	Hierarchically Compositional Kernels for Scalable Nonparametric Learning. J. Mach. Learn. Res. Jie Chen, Haim Avron, Vikas Sindhwani
2017	A*	`conf`	Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees. ICML Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh
2017	—	`conf`	Sharper Bounds for Regularized Data Fitting. APPROX-RANDOM Haim Avron, Kenneth L. Clarkson, David P. Woodruff
2016	J	`jnl`	Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. CoRR Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin
2016	J	`jnl`	Faster Kernel Ridge Regression Using Sketching and Preconditioning. CoRR Haim Avron, Kenneth L. Clarkson, David P. Woodruff
2016	J	`jnl`	Hierarchically Compositional Kernels for Scalable Nonparametric Learning. CoRR Jie Chen, Haim Avron, Vikas Sindhwani
2016	J	`jnl`	High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization. Technometrics Haim Avron, Vikas Sindhwani
2016	J	`jnl`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney
2016	J	`jnl`	Sharper Bounds for Regression and Low-Rank Approximation with Regularization. CoRR Haim Avron, Kenneth L. Clarkson, David P. Woodruff
2015	—	`conf`	A scalable randomized least squares solver for dense overdetermined systems. ScalA@SC Chander Iyer, Haim Avron, Georgios Kollias, Yves Ineichen, Christopher D. Carothers, Petros Drineas
2015	A*	`conf`	Community Detection Using Time-Dependent Personalized PageRank. ICML Haim Avron, Lior Horesh
2015	J	`jnl`	Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. J. ACM Haim Avron, Alex Druinsky, Anshul Gupta
2014	J	`jnl`	Efficient Dimensionality Reduction for Canonical Correlation Analysis. SIAM J. Sci. Comput. Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
2014	J	`jnl`	High-performance Kernel Machines with Implicit Distributed Optimization and Randomization. CoRR Vikas Sindhwani, Haim Avron
2014	Misc	`conf`	Kernel methods match Deep Neural Networks on TIMIT. ICASSP Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran
2014	A*	`conf`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
2014	J	`jnl`	Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney
2014	A*	`conf`	Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney
2014	A	`conf`	Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. IPDPS Haim Avron, Alex Druinsky, Anshul Gupta
2014	—	`conf`	Subspace Embeddings for the Polynomial Kernel. NIPS Haim Avron, Huy L. Nguyen, David P. Woodruff
2013	J	`jnl`	A Randomized Asynchronous Linear Solver with Provable Convergence Rate CoRR Haim Avron, Alex Druinsky, Anshul Gupta
2013	—	`conf`	Efficient Dimensionality Reduction for Canonical Correlation Analysis. ICML (1) Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
2013	J	`jnl`	Faster Subset Selection for Matrices and Applications. SIAM J. Matrix Anal. Appl. Haim Avron, Christos Boutsidis
2013	J	`jnl`	Reliable Iterative Condition-Number Estimation CoRR Haim Avron, Alex Druinsky, Sivan Toledo
2013	—	`conf`	Sketching Structured Matrices for Faster Nonlinear Regression. NIPS Haim Avron, Vikas Sindhwani, David P. Woodruff
2013	J	`jnl`	Solving Hermitian positive definite systems using indefinite incomplete factorizations. J. Comput. Appl. Math. Haim Avron, Anshul Gupta, Sivan Toledo
2012	J	`jnl`	Efficient Dimensionality Reduction for Canonical Correlation Analysis CoRR Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias
2012	A*	`conf`	Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani
2012	J	`jnl`	Faster Subset Selection for Matrices and Applications CoRR Haim Avron, Christos Boutsidis
2012	A	`conf`	Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers. SC Haim Avron, Anshul Gupta
2011	J	`jnl`	Effective Stiffness: Generalizing Effective Resistance Sampling to Finite Element Matrices CoRR Haim Avron, Sivan Toledo
2011	J	`jnl`	Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix. J. ACM Haim Avron, Sivan Toledo
2010	—	`—`	Advanced algorithmic techniques in numerical linear algebra: hybridization and randomization Haim Avron
2010	J	`jnl`	Blendenpik: Supercharging LAPACK's Least-Squares Solver. SIAM J. Sci. Comput. Haim Avron, Petar Maymounkov, Sivan Toledo
2009	J	`jnl`	Combinatorial Preconditioners for Scalar Elliptic Finite-Element Problems. SIAM J. Matrix Anal. Appl. Haim Avron, Doron Chen, Gil Shklarski, Sivan Toledo
2009	J	`jnl`	On Element SDD Approximability CoRR Haim Avron, Gil Shklarski, Sivan Toledo
2009	A	`conf`	PFunc: modern task parallelism for modern high performance computing. SC Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine
2009	J	`jnl`	Using Perturbed QR Factorizations to Solve Linear Least-Squares Problems. SIAM J. Matrix Anal. Appl. Haim Avron, Esmond Ng, Sivan Toledo
2008	J	`jnl`	Parallel unsymmetric-pattern multifrontal sparse LU with column preordering. ACM Trans. Math. Softw. Haim Avron, Gil Shklarski, Sivan Toledo