papers
Publications (12)
stat.ML2015
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization
Vikas Sindhwani, Haim Avron
cs.LG2018
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han, Haim Avron, Jinwoo Shin
math.NA2019
Sketching for Principal Component Regression
Liron Mor-Yosef, Haim Avron
cs.LG2017
Hierarchically Compositional Kernels for Scalable Nonparametric Learning
Jie Chen, Haim Avron, Vikas Sindhwani
cs.DS2017
Sharper Bounds for Regularized Data Fitting
Haim Avron, Kenneth L. Clarkson, David P. Woodruff
cs.DC2015
Revisiting Asynchronous Linear Solvers: Provable Convergence Rate Through Randomization
Haim Avron, Alex Druinsky, Anshul Gupta
stat.ML2017
Experimental Design for Non-Parametric Correction of Misspecified Dynamical Models
Gal Shulkind, Lior Horesh, Haim Avron
cs.LG2018
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco +3
cs.LG2012
Efficient and Practical Stochastic Subgradient Descent for Nuclear Norm Regularization
Haim Avron, Satyen Kale, Shiva Kasiviswanathan +1
cs.DS2017
Approximating the Spectral Sums of Large-scale Matrices using Chebyshev Approximations
Insu Han, Dmitry Malioutov, Haim Avron +1
cs.LG2018
Stable Tensor Neural Networks for Rapid Deep Learning
Elizabeth Newman, Lior Horesh, Haim Avron +1
stat.ML2017
Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid First-Principles Data-Driven Modeling
Remi R. Lam, Lior Horesh, Haim Avron +1