From inexact optimization to learning via gradient concentration
Bernhard Stankewitz (),
Nicole Mücke () and
Lorenzo Rosasco ()
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Bernhard Stankewitz: Humboldt University of Berlin
Nicole Mücke: Technical University Braunschweig
Lorenzo Rosasco: Universitá degli Studi di Genova
Computational Optimization and Applications, 2023, vol. 84, issue 1, No 10, 265-294
Abstract Optimization in machine learning typically deals with the minimization of empirical objectives defined by training data. The ultimate goal of learning, however, is to minimize the error on future data (test error), for which the training data provides only partial information. In this view, the optimization problems that are practically feasible are based on inexact quantities that are stochastic in nature. In this paper, we show how probabilistic results, specifically gradient concentration, can be combined with results from inexact optimization to derive sharp test error guarantees. By considering unconstrained objectives, we highlight the implicit regularization properties of optimization for learning.
Keywords: Implicit regularization; Kernel methods; Statistical learning (search for similar items in EconPapers)
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