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Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments.
Provable guarantees for gradient-based meta-learning
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 1902
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On the limited memory BFGS method for large scale optimization
Dong C Liu and Jorge Nocedal · 1989
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The geometry of algorithms with orthogonality constraints
Alan Edelman, Tomás A Arias, and Steven T Smith · 1998
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Metric entropy of the Grassmann manifold
Alain Pajor · 1998
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A model of inductive bias learning
Jonathan Baxter · 2000
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A framework for learning predictive structures from multiple tasks and unlabeled data
Rie Kubota Ando and Tong Zhang · 2005
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Introduction to Nonparametric Estimation
Alexandre B Tsybakov · 2008
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Tight oracle bounds for low-rank matrix recovery from a minimal number of noisy random measurements
EJ Candes and Y Plan · 2010
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Lower bounds for the minimax risk using f f -divergences, and applications
Adityanand Guntuboyina · 2011
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Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls
Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2011
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A simpler approach to matrix completion
Benjamin Recht · 2011
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Estimation of high-dimensional low-rank matrices
Angelika Rohde, Alexandre B Tsybakov, et al · 2011
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A method of moments for mixture models and hidden Markov models
Animashree Anandkumar, Daniel Hsu, and Sham M Kakade · 2012
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Random design analysis of ridge regression
Daniel Hsu, Sham M Kakade, and Tong Zhang · 2012
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User-friendly tail bounds for sums of random matrices
Joel A Tropp · 2012
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Matrix Analysis , volume 169
Rajendra Bhatia · 2013
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Excess risk bounds for multitask learning with trace norm regularization
Massimiliano Pontil and Andreas Maurer · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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No spurious local minima in nonconvex low rank problems: A unified geometric analysis
Rong Ge, Chi Jin, and Yi Zheng · 2017
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How to escape saddle points efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017
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Ray: A distributed framework for emerging { \{ AI } \} applications
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I Jordan, et al · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science , volume 47
Roman Vershynin · 2018
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Cloze-driven pretraining of self-attention networks
Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, and Michael Auli · 2019
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Lower bounds on the performance of polynomial-time algorithms for sparse linear regression
Yuchen Zhang, Martin J Wainwright, and Michael I Jordan · 2014
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Autograd: Effortless gradients in numpy
Dougal Maclaurin, David Duvenaud, and Ryan P Adams · 2015
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On bayes risk lower bounds
Xi Chen, Adityanand Guntuboyina, and Yuchen Zhang · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 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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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Learning-to-learn stochastic gradient descent with biased regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, and Massimiliano Pontil · 2019
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Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
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Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint , volume 48
Martin J Wainwright · 2019
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Characterizing and avoiding negative transfer
Zirui Wang, Zihang Dai, Barnabás Póczos, and Jaime Carbonell · 2019
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Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
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