Web: http://arxiv.org/abs/2011.07142

May 5, 2022, 1:10 a.m. | Abhishek Chakraborty, Ketan Rajawat, Alec Koppel

cs.CV updates on arXiv.org arxiv.org

We consider expected risk minimization problems when the range of the
estimator is required to be nonnegative, motivated by the settings of maximum
likelihood estimation (MLE) and trajectory optimization. To facilitate
nonlinear interpolation, we hypothesize that the search space is a Reproducing
Kernel Hilbert Space (RKHS). We develop first and second-order variants of
stochastic mirror descent employing (i) \emph{pseudo-gradients} and (ii)
complexity-reducing projections. Compressive projection in the first-order
scheme is executed via kernel orthogonal matching pursuit (KOMP), which
overcomes the …

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