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UID:8250@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20141114T130000
DTEND;TZID=Europe/Paris:20141114T140000
DTSTAMP:20241120T210312Z
URL:https://www.i2m.univ-amu.fr/evenements/a-bonnefoy-lif-dynamic-screenin
 g-principle-applied-to-the-lasso-and-group-lasso/
SUMMARY: (...): A. Bonnefoy (LIF): Dynamic Screening Principle applied to t
 he Lasso and Group-Lasso.
DESCRIPTION:: Dynamic Screening Principle applied to the Lasso and Group-La
 sso by Anthoine Bonnefoy (LIF)\n\n\nRecent computational strategies based 
 on screening tests have been proposed to accelerate algorithms addressing 
 penalized sparse regression problems such as the Lasso. Such approaches bu
 ild upon the idea that it is worth dedicating some small computational eff
 ort to locate inactive atoms and remove them from the dictionary in a prep
 rocessing stage so that the regression algorithm working with a smaller di
 ctionary will then converge faster to the solution of the initial problem.
  We believe that there is an even more efficient way to screen the diction
 ary and obtain a greater acceleration: inside each iteration of the regres
 sion algorithm\\\, one may take advantage of the algorithm computations to
  obtain a new screening test for free with increasing screening effects al
 ong the iterations. The dictionary is henceforth dynamically screened inst
 ead of being screened statically\\\, once and for all\\\, before the first
  iteration. We formalize this dynamic screening principle in a general alg
 orithmic scheme and apply it by embedding inside a number of first-order a
 lgorithms adapted existing screening tests to solve the Lasso or new scree
 ning tests to solve the Group-Lasso. Computational gains are assessed in a
  large set of experiments on synthetic data as well as real-world sounds a
 nd images. They show both the screening efficiency and the gain in terms r
 unning times.
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DTSTART:20141026T020000
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