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DTSTART:20211031T030000
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UID:calendar.23941.field_data.0@diag.uniroma1.it
DTSTAMP:20260404T175505Z
CREATED:20211212T170139Z
DESCRIPTION:Two-level stochastic optimization formulations have become inst
 rumental in a number of machine learning contexts such as neural architect
 ure search\, continual learning\, adversarial learning\, and hyperparamete
 r tuning.  Practical stochastic bilevel optimization problems become chall
 enging in optimization or learning scenarios where the number of variables
  is high or there are constraints.The goal of this work is twofold. First\
 , we aim at promoting the use of bilevel optimization in large-scale learn
 ing and we introduce a practical bilevel stochastic gradient method (BSG-1
 ) that requires neither lower level second-order derivatives nor system so
 lves (and dismisses any matrix-vector products). Our BSG-1 method is close
  to first-order principles\, which allows it to achieve a performance bett
 er than those that are not\, such as DARTS. Second\, we develop bilevel st
 ochastic gradient descent for bilevel problems with lower level constraint
 s\, and we introduce a convergence theory that covers the unconstrained an
 d constrained cases and abstracts as much as possible from the specifics o
 f the bilevel gradient calculation. Joint work with Griffin Kent and Luis 
 Nunes Vicente. Preprint: https://arxiv.org/abs/2110.00604  
DTSTART;TZID=Europe/Paris:20211221T110000
DTEND;TZID=Europe/Paris:20211221T110000
LAST-MODIFIED:20211212T181537Z
LOCATION:Aula Magna - DIAG
SUMMARY:Bilevel stochastic methods for optimization and machine learning: B
 ilevel stochastic descent and DARTS - Tommaso Giovannelli (Lehigh Universi
 ty\, Bethlehem\, PA\, USA)
URL;TYPE=URI:http://diag.uniroma1.it/node/23941
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