On Nonconvex Optimization for Machine Learning

A SMART STOCHASTIC ALGORITHM FOR NONCONVEX OPTIMIZATION A SMART Stochastic Algorithm for Nonconvex Optimization with Applications to Robust Machine Learning

Machine learning theory typically assumes that training data is unbiased and not adversarially generated. When real training data deviates from these assumptions, trained models make erroneous predictions, sometimes with disastrous effects. Robust losses, such as the huber norm, were designed to mitigate the effects of such contaminated data, but they are limited to the regression context. In t...

Nonconvex Optimization Using a Fokker-planck Learning Machine

A new algorithm for nonconvex optimization by means of a so-called Fokker-Planck Learning Machine is proposed in this paper. This is done by considering the Fokker-Planck (FP) equation related to continuous simulated annealing, which has been proven to convergence to the global optimum under certain conditions. An approximate solution to the FP equation is sought by parametrizing the transition...

Optimization for Machine Learning

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Optimization for Machine Learning

Optimization for Machine Learning