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In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks.
A Probabilistic Theory of Pattern Recognition
Devroye, L., Gyoörfi, L., and Lugosi, G · 1996
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Is learning the n-th thing any easier than learning the first?
Thrun, S · 1996
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Multitask learning
Caruana, R · 1997
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Learning To Learn
Thrun, S. and Pratt, L · 1997
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The mnist database of handwritten digits
LeCun, Y · 1998
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PAC-Bayesian model averaging
McAllester, D. A · 1999
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PAC-Bayesian model averaging
McAllester, D. A · 1999
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A model of inductive bias learning
Baxter, J · 2000
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PAC-Bayesian generalisation error bounds for gaussian process classification
Seeger, M · 2002
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A perspective view and survey of meta-learning
Vilalta, R. and Drissi, Y · 2002
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PAC-Bayesian generalisation error bounds for gaussian process classification
Seeger, M · 2002
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Latent Dirichlet allocation
Blei, D. M., Ng, A. Y., and Jordan, M. I · 2003
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A note on the PAC Bayesian theorem
Maurer, A · 2004
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Algorithmic stability and meta-learning
Maurer, A · 2005
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PAC-Bayesian supervised classification
Catoni, O · 2007
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Flexible latent variable models for multi-task learning
Zhang, J., Ghahramani, Z., and Yang, Y · 2008
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Transfer bounds for linear feature learning
Maurer, A · 2009
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PAC-Bayesian aggregation and multi-armed bandits
Audibert, J.-Y · 2010
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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Practical variational inference for neural networks
Graves, A · 2011
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PAC-Bayesian inequalities for martingales
Seldin, Y., Laviolette, F., Cesa-Bianchi, N., Shawe-Taylor, J., and Auer, P · 2012
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2013
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Tighter PAC-Bayes bounds through distribution-dependent priors
Lever, G., Laviolette, F., and Shawe-Taylor, J · 2013
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Towards a neural statistician
Edwards, H. and Storkey, A · 2016
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A theoretical framework for deep transfer learning
Galanti, T., Wolf, L., and Hazan, T · 2016
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PAC-Bayesian theory meets Bayesian inference
Germain, P., Bach, F., Lacoste, A., and Lacoste-Julien, S · 2016
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The benefit of multitask representation learning
Maurer, A., Pontil, M., and Romera-Paredes, B · 2016
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Fast and accurate deep network learning by exponential linear units (ELUs)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2016
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Towards a neural statistician
Edwards, H. and Storkey, A · 2016
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ELLA: An efficient lifelong learning algorithm
Ruvolo, P. and Eaton, E · 2013
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PAC-Bayes-empirical-Bernstein inequality
Tolstikhin, I. O. and Seldin, Y · 2013
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A PAC-Bayesian bound for lifelong learning
Pentina, A. and Lampert, C. H · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Understanding machine learning: From theory to algorithms
Shalev-Shwartz, S. and Ben-David, S · 2014
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PAC-Bayesian theory meets Bayesian inference
Germain, P., Bach, F., Lacoste, A., and Lacoste-Julien, S · 2016
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Regret Bounds for Lifelong Learning
Alquier, P., Mai, T. T., and Pontil, M · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
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Model-agnostic meta-learning for fast adaptation of deep networks
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Knowledge transfer for deep reinforcement learning with hierarchical experience replay
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Recasting gradient-based meta-learning as hierarchical Bayes
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Simpler PAC-Bayesian bounds for hostile data
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