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Meta-learning seeks to build algorithms that rapidly learn how to solve new learning problems based on previous experience.
A model of inductive bias learning
Jonathan Baxter · 2000
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Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
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On the Convergence of Eigenspaces in Kernel Principal Component Analysis
Laurent Zwald and Gilles Blanchard · 2005
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Concentration of the adjacency matrix and of the laplacian in random graphs with independent edges
Roberto Imbuzeiro Oliveira · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Excess risk bounds for multitask learning with trace norm regularization
Andreas Maurer and Massimiliano Pontil · 2013
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Concentration Inequalities for Sums and Martingales
Bernard Bercu, Bernard Delyon, and Emmanuel Rio · 2015
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Perturbation of linear forms of singular vectors under gaussian noise
Vladimir Koltchinskii and Dong Xia · 2016
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
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Incremental learning-to-learn with statistical guarantees
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, and Massimiliano Pontil · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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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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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina F Balcan, and Ameet S Talwalkar · 2019
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Online decision making with high-dimensional covariates
Hamsa Bastani and Mohsen Bayati · 2020
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Meta-learning with stochastic linear bandits
Multi-task and meta-learning with sparse linear bandits
Leonardo Cella and Massimiliano Pontil · 2021
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Near-optimal representation learning for linear bandits and linear rl
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, and Liwei Wang · 2021
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Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, and Csaba Szepesvari · 2021
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Sparsity-agnostic lasso bandit
Min-hwan Oh, Garud Iyengar, and Assaf Zeevi · 2021
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Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael Jordan · 2021
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Trace norm regularization for multi-task learning with scarce data
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Impact of representation learning in linear bandits
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Mostly exploration-free algorithms for contextual bandits
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Nearly minimax algorithms for linear bandits with shared representation
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