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Meta learning automatically infers an inductive bias, that includes the hyperparameter of the base-learning algorithm, by observing data from a finite number of related tasks.
A Primer on PAC-Bayesian Learning
Guedj, B · 1901
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Still no free lunches: the price to pay for tighter PAC-Bayes bounds
Guedj, B. and Pujol, L · 1910
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Machine Learning
Mitchell, T. M · 1997
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Learning to Learn: Introduction and Overview , pp. 3–17
Thrun, S. and Pratt, L · 1998
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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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PAC-Bayesian stochastic model selection
Mcallester, D. A · 2003
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A note on the PAC-Bayesian theorem
Maurer, A · 2004
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PAC-Bayes analysis beyond the usual bounds
Rivasplata, O., Kuzborskij, I., and Szepesvári, C · 2006
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PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning , volume 56
Catoni, O · 2007
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PAC-Bayesian bounds for randomized empirical risk minimizers
Alquier, P · 2008
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Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
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On Optimality of Meta-Learning in Fixed-Design Regression with Weighted Biased Regularization
Konobeev, M., Kuzborskij, I., and Szepesvári, C · 2011
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PAC-Bayesian tutorial with a dropout bound
McAllester, D · 2013
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A PAC-Bayesian bound for lifelong learning
Pentina, A. and Lampert, C · 2014
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Alquier, P., Ridgway, J., Chopin, N., and Teh, Y. W · 2016
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Controlling bias in adaptive data analysis using information theory
Russo, D. and Zou, J · 2016
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Finn, C., Abbeel, P., and Levine, S · 2017
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Tightening mutual information based bounds on generalization error
Bu, Y., Zou, S., and Veeravalli, V. V · 2019
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Learning-to-learn stochastic gradient descent with biased regularization
Denevi, G., Ciliberto, C., Grazzi, R., and Pontil, M · 2019
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Adaptive gradient-based meta-learning methods
Khodak, M., Balcan, M.-F. F., and Talwalkar, A. S · 2019
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Information-theoretic generalization bounds for SGLD via data-dependent estimates
Negrea, J., Haghifam, M., Dziugaite, G. K., Khisti, A., and Roy, D. M · 2019
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High-Dimensional Statistics: A Non-Asymptotic Viewpoint
Wainwright, M · 2019
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Li, Z., Zhou, F., Chen, F., and Li, H · 2017
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Optimization as a model for few-shot learning
Ravi, S. and Larochelle, H · 2017
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A strongly quasiconvex pac-bayesian bound
Thiemann, N., Igel, C., Wintenberger, O., and Seldin, Y · 2017
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Information-theoretic analysis of generalization capability of learning algorithms
Xu, A. and Raginsky, M · 2017
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Meta-learning by adjusting priors based on extended PAC-Bayes theory
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Denevi, G., Ciliberto, C., Stamos, D., and Pontil, M · 2018
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Information-theoretic generalization bounds for meta-learning and applications
Jose, S. T. and Simeone, O · 2021
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PAC-Bayes bounds for meta-learning with data-dependent prior
Liu, T., Lu, J., Yan, Z., and Zhang, G · 2021
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Novel change of measure inequalities with applications to PAC-Bayesian bounds and monte carlo estimation
Ohnishi, Y. and Honorio, J · 2021
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Conditional mutual information-based generalization bound for meta learning
Rezazadeh, A., Sharu, S. T., Durisi, G., and Simeone, O · 2021
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PACOH: Bayes-optimal meta-learning with PAC-guarantees
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