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Biased regularization and fine-tuning are two recent meta-learning approaches.
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A model of inductive bias learning
J. Baxter · 2000
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Regularized multi–task learning
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Convex multi-task feature learning
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Random features for large-scale kernel machines
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Clustered multi-task learning: A convex formulation
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Transfer bounds for linear feature learning
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Linear algorithms for online multitask classification
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Convex Analysis and Monotone Operator theory in Hilbert Spaces
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Transfer learning in a heterogeneous environment
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Learning the graph of relations among multiple tasks
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A PAC-Bayesian bound for lifelong learning
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Understanding Machine Learning: From Theory to Algorithms
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Learning-to-learn stochastic gradient descent with biased regularization
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Online-within-online meta-learning
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On the convergence theory of gradient-based model-agnostic meta-learning algorithms
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Online meta-learning
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Reconciling meta-learning and continual learning with online mixtures of tasks
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Adaptive gradient-based meta-learning methods
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Coin betting and parameter-free online learning
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Model-agnostic meta-learning for fast adaptation of deep networks
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Black-box reductions for parameter-free online learning in banach spaces
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Multimodal model-agnostic meta-learning via task-aware modulation
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Hierarchically structured meta-learning
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Weighted message passing and minimum energy flow for heterogeneous stochastic block models with side information
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