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Meta-learning leverages related source tasks to learn an initialization that can be quickly fine-tuned to a target task with limited labeled examples.
Hedonic housing prices and the demand for clean air
D. Harrison Jr and D. L. Rubinfeld · 1978
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Integral probability metrics and their generating classes of functions
A. Müller · 1997
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Rademacher and Gaussian complexities: Risk bounds and structural results
P. L. Bartlett and S. Mendelson · 2002
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Least angle regression
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Random features for large-scale kernel machines
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A theory of learning from different domains
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On the empirical estimation of integral probability metrics
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Generalization bounds for domain adaptation
C. Zhang, L. Zhang, and J. Ye · 2012
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Generalization bounds for domain adaptation
C. Zhang, L. Zhang, and J. Ye · 2013
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Domain adaptation and sample bias correction theory and algorithm for regression
C. Cortes and M. Mohri · 2014
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Time series clustering: A superior alternative for market basket analysis
S. C. Tan and J. P. San Lau · 2014
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Siamese neural networks for one-shot image recognition
G. Koch · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
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Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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Low data drug discovery with one-shot learning
H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Meta-SGD: Learning to learn quickly for few-shot learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
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Meta networks
T. Munkhdalai and H. Yu · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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On the discrimination-generalization tradeoff in GANs, 2018
P. Zhang, Q. Liu, D. Zhou, T. Xu, and X. He · 2018
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Task2vec: Task embedding for meta-learning
A. Achille, M. Lam, R. Tewari, A. Ravichandran, S. Maji, C. C. Fowlkes, S. Soatto, and P. Perona · 2019
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Learning GANs and ensembles using discrepancy
B. Adlam, C. Cortes, M. Mohri, and N. Zhang · 2019
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On the convergence theory of gradient-based model-agnostic meta-learning algorithms
A. Fallah, A. Mokhtari, and A. Ozdaglar · 2019
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Online meta-learning
C. Finn, A. Rajeswaran, S. Kakade, and S. Levine · 2019
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Reconciling meta-learning and continual learning with online mixtures of tasks
G. Jerfel, E. Grant, T. Griffiths, and K. A. Heller · 2019
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M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle · 2017
Cited alongside, same era.
How to train your maml
A. Antoniou, H. Edwards, and A. Storkey · 2018
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
C. Finn, K. Xu, and S. Levine · 2018
Cited alongside, same era.
Recasting gradient-based meta-learning as hierarchical Bayes
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths · 2018
Cited alongside, same era.
Deep online learning via meta-learning: Continual adaptation for model-based rl
A. Nagabandi, C. Finn, and S. Levine · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
A. Nichol, J. Achiam, and J. Schulman · 2018
Cited alongside, same era.
Dataset2vec: Learning dataset meta-features
H. S. Jomaa, J. Grabocka, and L. Schmidt-Thieme · 2019
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Multi-source domain adaptation with guarantees
A. Pentina, E. SDSC, and C. H. Lampert · 2019
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Amortized Bayesian meta-learning
S. Ravi and A. Beatson · 2019
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A principled approach for learning task similarity in multitask learning
C. Shui, M. Abbasi, L.-É. Robitaille, B. Wang, and C. Gagné · 2019
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Meta-learning via weighted gradient update
Z. Xu, L. Cao, and X. Chen · 2019
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Hierarchically structured meta-learning
H. Yao, Y. Wei, J. Huang, and Z. Li · 2019
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Metapred: Meta-learning for clinical risk prediction with limited patient electronic health records
X. S. Zhang, F. Tang, H. H. Dodge, J. Zhou, and F. Wang · 2019
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ES-MAML: Simple Hessian-free meta learning
X. Song, W. Gao, Y. Yang, K. Choromanski, A. Pacchiano, and Y. Tang · 2020
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