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Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks.
Learning a synaptic learning rule
Yoshua Bengio, Samy Bengio, and Jocelyn Cloutier · 1990
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Theoretical models of learning to learn
Jonathan Baxter · 1998
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Solving variational inequalities with stochastic mirror-prox algorithm
Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel · 2011
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Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
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Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application, 2013
Weiran Wang and Miguel A Carreira-Perpinán · 2013
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Statistics of robust optimization: A generalized empirical likelihood approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
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Mini-batch stochastic approximation methods for nonconvex stochastic composite optimization
Saeed Ghadimi, Guanghui Lan, and Hongchao Zhang · 2016
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Minimizing the maximal loss: How and why
Shai Shalev-Shwartz and Yonatan Wexler · 2016
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Robust optimization for non-convex objectives
Robert S Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Meta-sgd: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Meta-learning with differentiable closed-form solvers
Luca Bertinetto, Joao F Henriques, Philip Torr, and Andrea Vedaldi · 2018
Cited alongside, same era.
Learning models with uniform performance via distributionally robust optimization, 2018
John Duchi and Hongseok Namkoong · 2018
Cited alongside, same era.
Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Minmax optimization: Stable limit points of gradient descent ascent are locally optimal
Chi Jin, Praneeth Netrapalli, and Michael I Jordan · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
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Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
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Agnostic federated learning
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
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Solving a class of non-convex min-max games using iterative first order methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang, Jason D Lee, and Meisam Razaviyayn · 2019
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Robust optimization over multiple domains
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Cited alongside, same era.
Reptile: a scalable metalearning algorithm
Alex Nichol and John Schulman · 2018
Cited alongside, same era.
Non-convex min-max optimization: Provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2018
Cited alongside, same era.
Chengxiang Yin, Jian Tang, Zhiyuan Xu, and Yanzhi Wang · 2018
Cited alongside, same era.
How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos J. Storkey · 2019
Cited alongside, same era.
Provable guarantees for gradient-based meta-learning
Maria-Florina Balcan, Mikhail Khodak, and Ameet Talwalkar · 2019
Cited alongside, same era.
On the convergence theory of gradient-based model-agnostic meta-learning algorithms
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2019
Cited alongside, same era.
Qi Qian, Shenghuo Zhu, Jiasheng Tang, Rong Jin, Baigui Sun, and Hao Li · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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Es-maml: Simple hessian-free meta learning
Xingyou Song, Wenbo Gao, Yuxiang Yang, Krzysztof Choromanski, Aldo Pacchiano, and Yunhao Tang · 2019
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Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, et al · 2019
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Efficient meta learning via minibatch proximal update
Pan Zhou, Xiaotong Yuan, Huan Xu, Shuicheng Yan, and Jiashi Feng · 2019
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Online meta-learning on non-convex setting
Zhenxun Zhuang, Yunlong Wang, Kezi Yu, and Songtao Lu · 2019
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Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
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Diana Cai, Rishit Sheth, Lester Mackey, and Nicolo Fusi · 2020
Closest in time.
Learning to balance: Bayesian meta-learning for imbalanced and out-of-distribution tasks
Haebeom Lee, Hayeon Lee, Donghyun Na, Saehoon Kim, Minseop Park, Eunho Yang, and Sung Ju Hwang · 2020
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