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The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm.
Finn, C.; Rajeswaran, A.; Kakade, S.; and Levine, S. 2019 · 1902
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On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
Fallah, A.; Mokhtari, A.; and Ozdaglar, A. 2019 · 1908
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Zhang, K.; Hu, B.; and Basar, T. 2019 · 1910
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Principles of mathematical analysis (third edition)
Rudin, W. 1976 · 1976
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Nonlinear programming
Bertsekas, D. P.; Hager, W.; and Mangasarian, O. 1998 · 1998
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A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems
Todorov, E.; and Li, W. 2005 · 2005
Cited alongside, same era.
On the Global Optimality of Model-Agnostic Meta-Learning
Wang, L.; Cai, Q.; Yang, Z.; and Wang, Z. 2020 · 2006
Cited alongside, same era.
Dynamic Programming and Optimal Control
Bertsekas, D. P. 2017 · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
Cited alongside, same era.
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Fazel, M.; Ge, R.; Kakade, S.; and Mesbahi, M. 2018 · 2018
Cited alongside, same era.
A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization
Josz, C.; Ouyang, Y.; Zhang, R.; Lavaei, J.; and Sojoudi, S. 2018 · 2018
Later among the works it cites.
On first-order meta-learning algorithms
Nichol, A.; Achiam, J.; and Schulman, J. 2018 · 2018
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Learning deep models: Critical points and local openness
Nouiehed, M.; and Razaviyayn, M. 2018 · 2018
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Meta-learning with implicit gradients
Rajeswaran, A.; Finn, C.; Kakade, S. M.; and Levine, S. 2019 · 2019
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Efficient meta learning via minibatch proximal update
Zhou, P.; Yuan, X.; Xu, H.; Yan, S.; and Feng, J. 2019 · 2019
Later among the works it cites.
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