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Meta-learning can successfully acquire useful inductive biases from data.
Noise Regularization for Conditional Density Estimation
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On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
Thompson, W. R · 1933
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Thrun, S. and Pratt, L. (eds.) · 1998
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Some PAC-Bayesian theorems
McAllester, D. A · 1999
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A model of inductive bias learning
Baxter, J · 2000
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Learning To Learn Using Gradient Descent
Hochreiter, S., Younger, A. S., and Conwell, P. R · 2001
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Occam’s Razor
Rasmussen, C. E. and Ghahramani, Z · 2001
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Using confidence bounds for exploitation-exploration trade-offs
Auer, P · 2002
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Gaussian processes in machine learning
Rasmussen, C. E. and Williams, C. K. I · 2006
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PAC-Bayesian supervised classification: the thermodynamics of statistical learning
Catoni, O · 2007
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S. M., and Seeger, M · 2009
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Inferring latent task structure for multitask learning by multiple kernel learning
Widmer, C., Toussaint, N. C., Altun, Y., and Rätsch, G · 2010
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Contextual gaussian process bandit optimization
Krause, A. and Ong, C. S · 2011
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Tighter PAC-Bayes bounds through distribution-dependent priors
Lever, G., Laviolette, F., and Shawe-Taylor, J · 2012
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PAC-Bayes Bounds with Data Dependent Priors
Parrado-Hernandez, E., Ambroladze, A., Shawe-Taylor, J., and Sun, S · 2012
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Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012
Silva, I., Moody, G., Scott, D. J., Celi, L. A., and Mark, R. G · 2012
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
Bergstra, J., Yamins, D., and Cox, D · 2013
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Concentration inequalities : a nonasymptotic theory of independence
Boucheron, S., Lugosi, G., and Massart, P · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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A PAC-Bayesian bound for lifelong learning
Pentina, A. and Lampert, C · 2014
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S · 2015
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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Swissfel: the swiss x-ray free electron laser
Milne, C. J., Schietinger, T., Aiba, M., Alarcon, A., Alex, J., Anghel, A., Arsov, V., Beard, C., Beaud, P., Bettoni, S., et al · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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Meta-learning by adjusting priors based on extended PAC-Bayes theory
Amit, R. and Meir, R · 2018
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Probabilistic model-agnostic meta-learning
Finn, C., Xu, K., and Levine, S · 2018
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Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F., Rezende, D. J., Eslami, S., and Teh, Y. W · 2018
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On the properties of variational approximations of Gibbs posteriors
Alquier, P., Ridgway, J., Chopin, N., and Teh, Y. W · 2016
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Learning to learn by gradient descent by gradient descent
Andrychowicz, M., Denil, M., Colmenarejo, S. G., Hoffman, M. W., Pfau, D., Schaul, T., Shillingford, B., and De Freitas, N · 2016
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Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2016
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
Dziugaite, G. K. and Roy, D. M · 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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Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Liu, Q. and Wang, D · 2016
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Bayesian model-agnostic meta-learning
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Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
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On First-Order Meta-Learning Algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
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Rethink and redesign meta learning
Qin, Y., Zhang, W., Zhao, C., Wang, Z., Shi, H., Qi, G., Shi, J., and Lei, Z · 2018
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Deep Mean Functions for Meta-Learning in Gaussian Processes
Fortuin, V. and Rätsch, G · 2019
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Multivariate time series imputation with variational autoencoders
Fortuin, V., Rätsch, G., and Mandt, S · 2019
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A primer on PAC-Bayesian learning
Guedj, B · 2019
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Intel lab data
Madden, S · 2020
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Meta-learning without memorization
Yin, M., Tucker, G., Zhou, M., Levine, S., and Finn, C · 2020
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