3,560 research outputs found
SelfieBoost: A Boosting Algorithm for Deep Learning
We describe and analyze a new boosting algorithm for deep learning called
SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct
ensembles of classifiers, SelfieBoost boosts the accuracy of a single network.
We prove a convergence rate for SelfieBoost under some "SGD
success" assumption which seems to hold in practice
Exact finite approximations of average-cost countable Markov Decision Processes
For a countable-state Markov decision process we introduce an embedding which
produces a finite-state Markov decision process. The finite-state embedded
process has the same optimal cost, and moreover, it has the same dynamics as
the original process when restricting to the approximating set. The embedded
process can be used as an approximation which, being finite, is more convenient
for computation and implementation.Comment: Submitted to Automatic
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization
We introduce a proximal version of the stochastic dual coordinate ascent
method and show how to accelerate the method using an inner-outer iteration
procedure. We analyze the runtime of the framework and obtain rates that
improve state-of-the-art results for various key machine learning optimization
problems including SVM, logistic regression, ridge regression, Lasso, and
multiclass SVM. Experiments validate our theoretical findings
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