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Bilevel optimization has been widely applied in many important machine learning applications such as hyperparameter optimization and meta-learning.
New branch-and-bound rules for linear bilevel programming
P. Hansen, B. Jaumard, and G. Savard · 1992
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Actor-critic algorithms
V. R. Konda and J. N. Tsitsiklis · 2000
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An extended kuhn-Tucker approach for linear bilevel programming
C. Shi, J. Lu, and G. Zhang · 2005
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Bilevel programming algorithms for machine learning model selection
G. M. Moore · 2010
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Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
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Gradient-based hyperparameter optimization through reversible learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
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S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo · 2016
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Hyperparameter optimization with approximate gradient
F. Pedregosa · 2016
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Forward and reverse gradient-based hyperparameter optimization
L. Franceschi, M. Donini, P. Frasconi, and M. Pontil · 2017
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SARAH: A novel method for machine learning problems using stochastic recursive gradient
L. M. Nguyen, J. Liu, K. Scheinberg, and M. Takáč · 2017
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Meta-learning with differentiable closed-form solvers
L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi · 2018
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Spider: Near-optimal non-convex optimization via stochastic path integrated differential estimator
C. Fang, C. J. Li, Z. Lin, and T. Zhang · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
L. Franceschi, P. Frasconi, S. Salzo, R. Grazzi, and M. Pontil · 2018
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Approximation methods for bilevel programming
S. Ghadimi and M. Wang · 2018
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A simple proximal stochastic gradient method for nonsmooth nonconvex optimization
Z. Li and J. Li · 2018
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Spiderboost: A class of faster variance-reduced algorithms for nonconvex optimization
Z. Wang, K. Ji, Y. Zhou, Y. Liang, and V. Tarokh · 2018
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Stochastic nested variance reduced gradient descent for nonconvex optimization
D. Zhou, P. Xu, and Q. Gu · 2018
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Momentum-based variance reduction in non-convex SGD
A. Cutkosky and F. Orabona · 2019
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Convergence of meta-learning with task-specific adaptation over partial parameters
K. Ji, J. D. Lee, Y. Liang, and H. V. Poor · 2020
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Multi-step model-agnostic meta-learning: Convergence and improved algorithms
K. Ji, J. Yang, and Y. Liang · 2020
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Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems
L. Luo, H. Ye, Z. Huang, and T. Zhang · 2020
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Gradient free minimax optimization: Variance reduction and faster convergence
T. Xu, Z. Wang, Y. Liang, and H. V. Poor · 2020
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Global convergence and variance reduction for a class of nonconvex-nonconcave minimax problems
J. Yang, N. Kiyavash, and N. He · 2020
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Hyperparameter optimization
M. Feurer and F. Hutter · 2019
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Improved zeroth-order variance reduced algorithms and analysis for nonconvex optimization
K. Ji, Z. Wang, Y. Zhou, and Y. Liang · 2019
Cited alongside, same era.
Meta-learning with implicit gradients
A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine · 2019
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Truncated back-propagation for bilevel optimization
A. Shaban, C.-A. Cheng, N. Hatch, and B. Boots · 2019
Cited alongside, same era.
Hybrid stochastic gradient descent algorithms for stochastic nonconvex optimization
Q. Tran-Dinh, N. H. Pham, D. T. Phan, and L. M. Nguyen · 2019
Cited alongside, same era.
Spiderboost and momentum: Faster variance reduction algorithms
Z. Wang, K. Ji, Y. Zhou, Y. Liang, and V. Tarokh · 2019
Cited alongside, same era.
On the iteration complexity of hypergradient computation
R. Grazzi, L. Franceschi, M. Pontil, and S. Salzo · 2020
Cited alongside, same era.
A single-timescale stochastic bilevel optimization method
T. Chen, Y. Sun, and W. Yin · 2021
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On stochastic moving-average estimators for non-convex optimization
Z. Guo, Y. Xu, W. Yin, R. Jin, and T. Yang · 2021
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Randomized stochastic variance-reduced methods for stochastic bilevel optimization
Z. Guo and T. Yang · 2021
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Lower bounds and accelerated algorithms for bilevel optimization
K. Ji and Y. Liang · 2021
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Bilevel optimization: Nonasymptotic analysis and faster algorithms
K. Ji, J. Yang, and Y. Liang · 2021
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A momentum-assisted single-timescale stochastic approximation algorithm for bilevel optimization
P. Khanduri, S. Zeng, M. Hong, H.-T. Wai, Z. Wang, and Z. Yang · 2021
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A near-optimal algorithm for stochastic bilevel optimization via double-momentum
P. Khanduri, S. Zeng, M. Hong, H.-T. Wai, Z. Wang, and Z. Yang · 2021
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Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
H. Rafique, M. Liu, Q. Lin, and T. Yang · 2021
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