qNBO coordinates lower-level quasi-Newton iterations with inverse Hessian-vector products to approximate bilevel hypergradients, giving BFGS and SR1 instantiations and a non-asymptotic BFGS convergence rate.
Amortized implicit differentiation for stochastic bilevel optimization
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
qNBO: quasi-Newton Meets Bilevel Optimization
qNBO coordinates lower-level quasi-Newton iterations with inverse Hessian-vector products to approximate bilevel hypergradients, giving BFGS and SR1 instantiations and a non-asymptotic BFGS convergence rate.