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ORCIDHaim Avron
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2020 – today
- 2024
[j29]Elizabeth Newman, Lior Horesh, Haim Avron
, Misha E. Kilmer:
Stable tensor neural networks for efficient deep learning. Frontiers Big Data 7 (2024)- [j28]Shany Shmueli, Petros Drineas, Haim Avron:
Low-rank updates of matrix square roots. Numer. Linear Algebra Appl. 31(1) (2024) - [i42]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:
Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. CoRR abs/2401.13754 (2024) - [i41]Oria Gruber, Haim Avron:
On the Role of Initialization on the Implicit Bias in Deep Linear Networks. CoRR abs/2402.02454 (2024) - [i40]Liron Mor-Yosef, Shashanka Ubaru, Lior Horesh, Haim Avron:
Multivariate trace estimation using quantum state space linear algebra. CoRR abs/2405.01098 (2024) - 2023
- [j27]Boris Shustin, Haim Avron:
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold. J. Comput. Appl. Math. 423: 114953 (2023) - [j26]Uria Mor, Boris Shustin, Haim Avron:
Solving trust region subproblems using Riemannian optimization. Numerische Mathematik 154(1-2): 1-33 (2023) - [j25]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): 11766-11777 (2023) - [c24]Amir Zandieh, Insu Han, Haim Avron:
Near Optimal Reconstruction of Spherical Harmonic Expansions. NeurIPS 2023 - [i39]Raphael A. Meyer, Haim Avron:
Hutchinson's Estimator is Bad at Kronecker-Trace-Estimation. CoRR abs/2309.04952 (2023) - 2022
- [j24]Ron Levie, Haim Avron:
Randomized continuous frames in time-frequency analysis. Adv. Comput. Math. 48(3): 25 (2022) - [j23]Paz Fink Shustin, Haim Avron:
Gauss-Legendre Features for Gaussian Process Regression. J. Mach. Learn. Res. 23: 92:1-92:47 (2022) - [j22]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: 336:1-336:48 (2022) - [j21]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) - [j20]Paz Fink Shustin, Haim Avron:
Semi-Infinite Linear Regression and Its Applications. SIAM J. Matrix Anal. Appl. 43(1): 479-511 (2022) - [c23]Gregory Dexter, Agniva Chowdhury, Haim Avron, Petros Drineas:
On the Convergence of Inexact Predictor-Corrector Methods for Linear Programming. ICML 2022: 5007-5038 - [c22]Insu Han, Amir Zandieh, Haim Avron:
Random Gegenbauer Features for Scalable Kernel Methods. ICML 2022: 8330-8358 - [i38]Shany Shumeli, Petros Drineas, Haim Avron:
Low-Rank Updates of Matrix Square Roots. CoRR abs/2201.13156 (2022) - [i37]Insu Han, Amir Zandieh, Haim Avron:
Random Gegenbauer Features for Scalable Kernel Methods. CoRR abs/2202.03474 (2022) - [i36]Paz Fink Shustin, Shashanka Ubaru, Vasileios Kalantzis, Lior Horesh, Haim Avron:
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty. CoRR abs/2202.05063 (2022) - [i35]Amir Zandieh, Insu Han, Haim Avron:
Near Optimal Reconstruction of Spherical Harmonic Expansions. CoRR abs/2202.12995 (2022) - [i34]Boris Shustin, Haim Avron, Barak Sober:
Manifold Free Riemannian Optimization. CoRR abs/2209.03269 (2022) - [i33]Agniva Chowdhury, Gregory Dexter, Palma London, Haim Avron, Petros Drineas:
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs. CoRR abs/2209.08722 (2022) - 2021
- [c21]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: 1-7 - [c20]Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. NeurIPS 2021: 1062-1073 - [c19]Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron:
Dynamic Graph Convolutional Networks Using the Tensor M-Product. SDM 2021: 729-737 - [i32]Paz Fink Shustin, Haim Avron:
Gauss-Legendre Features for Gaussian Process Regression. CoRR abs/2101.01137 (2021) - [i31]Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Random Features for the Neural Tangent Kernel. CoRR abs/2104.01351 (2021) - [i30]Paz Fink Shustin, Haim Avron:
Semi-Infinite Linear Regression and Its Applications. CoRR abs/2104.05687 (2021) - [i29]Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, Jinwoo Shin:
Scaling Neural Tangent Kernels via Sketching and Random Features. CoRR abs/2106.07880 (2021) - [i28]Boris Shustin, Haim Avron:
Faster Randomized Methods for Orthogonality Constrained Problems. CoRR abs/2106.12060 (2021) - [i27]Uria Mor, Yotam Cohen, Rafael Valdes-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron:
Dimensionality Reduction of Longitudinal 'Omics Data using Modern Tensor Factorization. CoRR abs/2111.14159 (2021) - 2020
- [c18]Insu Han, Haim Avron, Jinwoo Shin:
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. ICML 2020: 3984-3993 - [c17]Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas:
Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs. NeurIPS 2020 - [i26]Misha E. Kilmer, Lior Horesh, Haim Avron, Elizabeth Newman:
Tensor-Tensor Products for Optimal Representation and Compression. CoRR abs/2001.00046 (2020) - [i25]Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas:
Speeding up Linear Programming using Randomized Linear Algebra. CoRR abs/2003.08072 (2020) - [i24]Ron Levie, Haim Avron:
Randomized Continuous Frames in Time-Frequency Analysis. CoRR abs/2009.10525 (2020) - [i23]Neta Shoham, Haim Avron:
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning. CoRR abs/2009.12820 (2020) - [i22]Uria Mor, Haim Avron:
Solving Trust Region Subproblems Using Riemannian Optimization. CoRR abs/2010.07547 (2020) - [i21]Ron Levie, Haim Avron, Gitta Kutyniok:
Quasi Monte Carlo Time-Frequency Analysis. CoRR abs/2011.02025 (2020)
2010 – 2019
- 2019
- [j19]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) - [j18]Haim Avron, Alex Druinsky, Sivan Toledo:
Spectral condition-number estimation of large sparse matrices. Numer. Linear Algebra Appl. 26(3) (2019) - [j17]Liron Mor-Yosef, Haim Avron:
Sketching for Principal Component Regression. SIAM J. Matrix Anal. Appl. 40(2): 454-485 (2019) - [c16]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: 1051-1063 - [i20]Boris Shustin, Haim Avron:
Randomized Riemannian Preconditioning for Quadratically Constrained Problems. CoRR abs/1902.01635 (2019) - [i19]Insu Han, Haim Avron, Jinwoo Shin:
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix. CoRR abs/1905.11616 (2019) - [i18]Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron:
Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs. CoRR abs/1910.07643 (2019) - 2018
- [j16]Gal Shulkind, Lior Horesh, Haim Avron:
Experimental Design for Nonparametric Correction of Misspecified Dynamical Models. SIAM/ASA J. Uncertain. Quantification 6(2): 880-906 (2018) - [c15]Insu Han, Haim Avron, Jinwoo Shin:
Stochastic Chebyshev Gradient Descent for Spectral Optimization. NeurIPS 2018: 7397-7407 - [i17]Insu Han, Haim Avron, Jinwoo Shin:
Optimizing Spectral Sums using Randomized Chebyshev Expansions. CoRR abs/1802.06355 (2018) - [i16]Liron Mor-Yosef, Haim Avron:
Sketching for Principal Component Regression. CoRR abs/1803.02661 (2018) - [i15]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 abs/1804.09893 (2018) - [i14]Elizabeth Newman, Lior Horesh, Haim Avron, Misha E. Kilmer:
Stable Tensor Neural Networks for Rapid Deep Learning. CoRR abs/1811.06569 (2018) - [i13]Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh:
A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms. CoRR abs/1812.08723 (2018) - 2017
- [j15]Jie Chen, Haim Avron, Vikas Sindhwani:
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. J. Mach. Learn. Res. 18: 66:1-66:42 (2017) - [j14]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Faster Kernel Ridge Regression Using Sketching and Preconditioning. SIAM J. Matrix Anal. Appl. 38(4): 1116-1138 (2017) - [j13]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) - [c14]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Sharper Bounds for Regularized Data Fitting. APPROX-RANDOM 2017: 27:1-27:22 - [c13]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: 253-262 - 2016
- [j12]Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. J. Mach. Learn. Res. 17: 120:1-120:38 (2016) - [j11]Haim Avron, Vikas Sindhwani:
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization. Technometrics 58(3): 341-349 (2016) - [i12]Insu Han, Dmitry Malioutov, Haim Avron, Jinwoo Shin:
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations. CoRR abs/1606.00942 (2016) - [i11]Jie Chen, Haim Avron, Vikas Sindhwani:
Hierarchically Compositional Kernels for Scalable Nonparametric Learning. CoRR abs/1608.00860 (2016) - [i10]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Faster Kernel Ridge Regression Using Sketching and Preconditioning. CoRR abs/1611.03220 (2016) - [i9]Haim Avron, Kenneth L. Clarkson, David P. Woodruff:
Sharper Bounds for Regression and Low-Rank Approximation with Regularization. CoRR abs/1611.03225 (2016) - 2015
- [j10]Haim Avron, Alex Druinsky, Anshul Gupta:
Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. J. ACM 62(6): 51:1-51:27 (2015) - [c12]Haim Avron, Lior Horesh:
Community Detection Using Time-Dependent Personalized PageRank. ICML 2015: 1795-1803 - [c11]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: 3:1-3:8 - 2014
- [j9]Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias:
Efficient Dimensionality Reduction for Canonical Correlation Analysis. SIAM J. Sci. Comput. 36(5) (2014) - [c10]Jiyan Yang, Vikas Sindhwani, Quanfu Fan, Haim Avron, Michael W. Mahoney:
Random Laplace Feature Maps for Semigroup Kernels on Histograms. CVPR 2014: 971-978 - [c9]Po-Sen Huang, Haim Avron, Tara N. Sainath, Vikas Sindhwani, Bhuvana Ramabhadran:
Kernel methods match Deep Neural Networks on TIMIT. ICASSP 2014: 205-209 - [c8]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. ICML 2014: 485-493 - [c7]Haim Avron, Alex Druinsky, Anshul Gupta:
Revisiting Asynchronous Linear Solvers: Provable Convergence Rate through Randomization. IPDPS 2014: 198-207 - [c6]Haim Avron, Huy L. Nguyen, David P. Woodruff:
Subspace Embeddings for the Polynomial Kernel. NIPS 2014: 2258-2266 - [i8]Vikas Sindhwani, Haim Avron:
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization. CoRR abs/1409.0940 (2014) - [i7]Jiyan Yang, Vikas Sindhwani, Haim Avron, Michael W. Mahoney:
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels. CoRR abs/1412.8293 (2014) - 2013
- [j8]Haim Avron, Anshul Gupta, Sivan Toledo:
Solving Hermitian positive definite systems using indefinite incomplete factorizations. J. Comput. Appl. Math. 243: 126-138 (2013) - [j7]Haim Avron, Christos Boutsidis:
Faster Subset Selection for Matrices and Applications. SIAM J. Matrix Anal. Appl. 34(4): 1464-1499 (2013) - [c5]Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias:
Efficient Dimensionality Reduction for Canonical Correlation Analysis. ICML (1) 2013: 347-355 - [c4]Haim Avron, Vikas Sindhwani, David P. Woodruff:
Sketching Structured Matrices for Faster Nonlinear Regression. NIPS 2013: 2994-3002 - [i6]Haim Avron, Alex Druinsky, Sivan Toledo:
Reliable Iterative Condition-Number Estimation. CoRR abs/1301.1107 (2013) - [i5]Haim Avron, Alex Druinsky, Anshul Gupta:
A Randomized Asynchronous Linear Solver with Provable Convergence Rate. CoRR abs/1304.6475 (2013) - 2012
- [c3]Haim Avron, Satyen Kale, Shiva Prasad Kasiviswanathan, Vikas Sindhwani:
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization. ICML 2012 - [c2]Haim Avron, Anshul Gupta:
Managing data-movement for effective shared-memory parallelization of out-of-core sparse solvers. SC 2012: 102 - [i4]Haim Avron, Christos Boutsidis:
Faster Subset Selection for Matrices and Applications. CoRR abs/1201.0127 (2012) - [i3]Haim Avron, Christos Boutsidis, Sivan Toledo, Anastasios Zouzias:
Efficient Dimensionality Reduction for Canonical Correlation Analysis. CoRR abs/1209.2185 (2012) - 2011
- [j6]Haim Avron, Sivan Toledo:
Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix. J. ACM 58(2): 8:1-8:34 (2011) - [i2]Haim Avron, Sivan Toledo:
Effective Stiffness: Generalizing Effective Resistance Sampling to Finite Element Matrices. CoRR abs/1110.4437 (2011) - 2010
- [b1]Haim Avron:
Advanced algorithmic techniques in numerical linear algebra: hybridization and randomization. Tel Aviv University, Israel, 2010 - [j5]Haim Avron, Petar Maymounkov, Sivan Toledo:
Blendenpik: Supercharging LAPACK's Least-Squares Solver. SIAM J. Sci. Comput. 32(3): 1217-1236 (2010) - [j4]Haim Avron, Andrei Sharf, Chen Greif, Daniel Cohen-Or:
l1-Sparse reconstruction of sharp point set surfaces. ACM Trans. Graph. 29(5): 135:1-135:12 (2010)
2000 – 2009
- 2009
- [j3]Haim Avron, Esmond Ng, Sivan Toledo:
Using Perturbed QR Factorizations to Solve Linear Least-Squares Problems. SIAM J. Matrix Anal. Appl. 31(2): 674-693 (2009) - [j2]Haim Avron, Doron Chen, Gil Shklarski, Sivan Toledo:
Combinatorial Preconditioners for Scalar Elliptic Finite-Element Problems. SIAM J. Matrix Anal. Appl. 31(2): 694-720 (2009) - [c1]Prabhanjan Kambadur, Anshul Gupta, Amol Ghoting, Haim Avron, Andrew Lumsdaine:
PFunc: modern task parallelism for modern high performance computing. SC 2009 - [i1]Haim Avron, Gil Shklarski, Sivan Toledo:
On Element SDD Approximability. CoRR abs/0911.0547 (2009) - 2008
- [j1]Haim Avron, Gil Shklarski, Sivan Toledo:
Parallel unsymmetric-pattern multifrontal sparse LU with column preordering. ACM Trans. Math. Softw. 34(2): 8:1-8:31 (2008)
Coauthor Index
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last updated on 2024-10-07 22:19 CEST by the dblp team
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