Fetching the paper…
Reading the bibliography…
We study a class of algorithms for solving bilevel optimization problems in both stochastic and deterministic settings when the inner-level objective is strongly convex.
MarktformundGleichgewicht
H.F. Von Stackelberg · 1934
Earlier work this paper cites.
Stabilite asyptotique pour des problemes de perturbations singulieres
P Habets · 1974
Earlier work this paper cites.
Quadratic-type lyapunov functions for singularly perturbed systems
Ali Saberi and Hassan Khalil · 1984
Earlier work this paper cites.
An introduction to the conjugate gradient method without the agonizing pain, 1994
Jonathan Richard Shewchuk et al · 1994
Earlier work this paper cites.
Optimality conditions for bilevel programming problems
JJ Ye and DL Zhu · 1995
Earlier work this paper cites.
Necessary optimality conditions for optimization problems with variational inequality constraints
JJ Ye and XY Ye · 1997
Earlier work this paper cites.
Exact penalization and necessary optimality conditions for generalized bilevel programming problems
JJ Ye, DL Zhu, and Qiji Jim Zhu · 1997
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course , volume 87
Yurii Nesterov · 2003
Earlier work this paper cites.
Designing and learning trainable priors with non-cooperative games
Bruno Lecouat, Jean Ponce, and Julien Mairal · 2006
Earlier work this paper cites.
Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang · 2007
Earlier work this paper cites.
Mingyi Hong, Hoi-To Wai, Zhaoran Wang, and Zhuoran Yang · 2007
Earlier work this paper cites.
Task-driven dictionary learning
Julien Mairal, Francis Bach, and Jean Ponce · 2011
Earlier work this paper cites.
Generic methods for optimization-based modeling
Justin Domke · 2012
Earlier work this paper cites.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
Saeed Ghadimi and Guanghui Lan · 2012
Earlier work this paper cites.
Fundamentals of differential geometry , volume 191
Serge Lang · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Stephen Gould, Basura Fernando, Anoop Cherian, Peter Anderson, Rodrigo Santa Cruz, and Edison Guo · 2016
Cited alongside, same era.
Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
Cited alongside, same era.
Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
Cited alongside, same era.
Minimizing finite sums with the stochastic average gradient
Mark Schmidt, Nicolas Le Roux, and Francis Bach · 2017
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
Cited alongside, same era.
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
Later among the works it cites.
Truncated back-propagation for bilevel optimization
Amirreza Shaban, Ching-An Cheng, Nathan Hatch, and Byron Boots · 2019
Later among the works it cites.
Super-efficiency of automatic differentiation for functions defined as a minimum
Pierre Ablin, Gabriel Peyré, and Thomas Moreau · 2020
Later among the works it cites.
Bilevel Optimization
Stephan Dempe and Alain Zemkoho · 2020
Later among the works it cites.
Nonlinear two-time-scale stochastic approximation: Convergence and finite-time performance
Thinh T Doan · 2020
Later among the works it cites.
On the iteration complexity of hypergradient computation
Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, and Saverio Salzo · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi · 2018
Cited alongside, same era.
Damek Davis and Dmitriy Drusvyatskiy · 2018
Cited alongside, same era.
Subgradient methods for sharp weakly convex functions
Damek Davis, Dmitriy Drusvyatskiy, Kellie J MacPhee, and Courtney Paquette · 2018
Cited alongside, same era.
Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
Cited alongside, same era.
Approximation methods for bilevel programming
Saeed Ghadimi and Mengdi Wang · 2018
Cited alongside, same era.
Reviving and improving recurrent back-propagation
Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, and Richard Zemel · 2018
Cited alongside, same era.
Catalyst acceleration for first-order convex optimization: from theory to practice
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui · 2018
Cited alongside, same era.
Later among the works it cites.
Finite time analysis of linear two-timescale stochastic approximation with markovian noise
Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, and Hoi-To Wai · 2020
Later among the works it cites.
Estimate sequences for stochastic composite optimization: Variance reduction, acceleration, and robustness to noise
Andrei Kulunchakov and Julien Mairal · 2020
Later among the works it cites.
Optimizing millions of hyperparameters by implicit differentiation
Jonathan Lorraine, Paul Vicol, and David Duvenaud · 2020
Later among the works it cites.
Efficient and modular implicit differentiation
Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, and Jean-Philippe Vert · 2021
Closest in time.
Convergence properties of stochastic hypergradients
Riccardo Grazzi, Massimiliano Pontil, and Saverio Salzo · 2021
Closest in time.
Lower bounds and accelerated algorithms for bilevel optimization
Kaiyi Ji and Yingbin Liang · 2021
Closest in time.
Bilevel optimization: Convergence analysis and enhanced design
Kaiyi Ji, Junjie Yang, and Yingbin Liang · 2021
Closest in time.
Risheng Liu, Jiaxin Gao, Jin Zhang, Deyu Meng, and Zhouchen Lin · 2021
Closest in time.
Provably faster algorithms for bilevel optimization
Junjie Yang, Kaiyi Ji, and Yingbin Liang · 2021
Closest in time.