Cristóbal Guzmán

Cristóbal Guzmán

Especialidad: Optimización, machine Learning, privacidad de datos.
Cristóbal es ingeniero civil matemático de la Universidad de Chile y PhD del Georgia Institute of Technology. Actualmente se desempeña como profesor en el Instituto de Ingeniería Matemática y Computacional de la Pontificia Universidad Católica de Chile.

PUBLICACIONES

Publisher: SIAM Journal on Optimization  Link>

ABSTRACT

Inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. This problem corresponds to the minimization of the sum of a (weakly) smooth function endowed with a stochastic first-order oracle and a structured uniformly convex (possibly nonsmooth and non-Lipschitz) regularization term. Despite intensive work on closely related settings, prior to our work no complexity bounds for this problem were known. We close this gap by providing novel excess risk bounds, both in expectation and with high probability. Our algorithms are nearly optimal, which we prove via novel lower complexity bounds for this class of problems. We conclude by providing numerical results comparing our methods to the state of the art.

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