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As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness.
Mean value theorems for vector valued functions
Robert M McLeod · 1965
Earlier work this paper cites.
Learning a synaptic learning rule
Y Bengio, S Bengio, and J Cloutier · 1991
Earlier work this paper cites.
Meta-neural networks that learn by learning
Devang K Naik and Richard J Mammone · 1992
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Infinite-horizon policy-gradient estimation
Jonathan Baxter and Peter L Bartlett · 2001
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Provably convergent policy gradient methods for model-agnostic meta-reinforcement learning
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2002
Earlier work this paper cites.
CAML: Fast context adaptation via meta-learning
Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2002
Earlier work this paper cites.
Global convergence and induced kernels of gradient-based meta-learning with neural nets
Haoxiang Wang, Ruoyu Sun, and Bo Li · 2006
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Regret bounds for lifelong learning
Pierre Alquier, Massimiliano Pontil, et al · 2017
Earlier work this paper cites.
Meta-SGD: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
Earlier work this paper cites.
Meta networks
Tsendsuren Munkhdalai and Hong Yu · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2018
Cited alongside, same era.
Federated meta-learning for recommendation
Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
Cited alongside, same era.
Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm
Chelsea Finn and Sergey Levine · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
Taming MAML: Efficient unbiased meta-reinforcement learning
Hao Liu, Richard Socher, and Caiming Xiong · 2019
Later among the works it cites.
Meta-learning for low-resource natural language generation in task-oriented dialogue systems
Fei Mi, Minlie Huang, Jiyong Zhang, and Boi Faltings · 2019
Later among the works it cites.
Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
Later among the works it cites.
ProMP: Proximal meta-policy search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2019
Later among the works it cites.
Efficient meta learning via minibatch proximal update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, and Jiashi Feng · 2019
Later among the works it cites.
Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon S Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
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Chelsea Finn, Kelvin Xu, and Sergey Levine · 2018
Cited alongside, same era.
DiCE: The infinitely differentiable monte carlo estimator
Jakob Foerster, Gregory Farquhar, Maruan Al-Shedivat, Tim Rocktäschel, Eric Xing, and Shimon Whiteson · 2018
Cited alongside, same era.
Recasting gradient-based meta-learning as hierarchical bayes
Erin Grant, Chelsea Finn, Sergey Levine, Trevor Darrell, and Thomas Griffiths · 2018
Cited alongside, same era.
Online gradient-based mixtures for transfer modulation in meta-learning
Ghassen Jerfel, Erin Grant, Thomas L Griffiths, and Katherine Heller · 2018
Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
Cited alongside, same era.
How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos Storkey · 2019
Cited alongside, same era.
Closest in time.
Distribution-agnostic model-agnostic meta-learning
Liam Collins, Aryan Mokhtari, and Sanjay Shakkottai · 2020
Closest in time.
Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
Closest in time.
Convergence of meta-learning with task-specific adaptation over partial parameters
Kaiyi Ji, Jason D Lee, Yingbin Liang, and H Vincent Poor · 2020
Closest in time.
Multi-step estimation for gradient-based meta-learning
Jin-Hwa Kim, Junyoung Park, and Yongseok Choi · 2020
Closest in time.
UFO-BLO: Unbiased first-order bilevel optimization
Valerii Likhosherstov, Xingyou Song, Krzysztof Choromanski, Jared Davis, and Adrian Weller · 2020
Closest in time.
Rapid learning or feature reuse? towards understanding the effectiveness of MAML
Aniruddh Raghu, Maithra Raghu, Samy Bengio, and Oriol Vinyals · 2020
Closest in time.
ES-MAML: Simple hessian-free meta learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Choromanski Krzysztof, Aldo Pacchiano, and Yunhao Tang · 2020
Closest in time.
Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2020
Closest in time.