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Approximate Stochastic Subgradient Estimation Training for Support Vector Machines
Sangkyun Lee, Stephen J. Wright
Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to l…
HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Feng Niu, Benjamin Recht, Christopher Re +1
Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently pro…
Convex Approaches to Model Wavelet Sparsity Patterns
Nikhil S Rao, Robert D. Nowak, Stephen J. Wright +1
Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees(HMTs). However, in linear inverse problems such as deco…