PAC-Bayesian Transportation Bound

Dropout: a simple way to prevent neural networks from overfitting

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014) · 1958

Earlier work this paper cites.

The sizes of compact subsets of hilbert space and continuity of gaussian processes

Dudley, R. M. (1967) · 1967

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Asymptotic evaluation of certain markov process expectations for large time, i

Donsker, M. D. and Varadhan, S. S. (1975) · 1975

Earlier work this paper cites.

Some pac-bayesian theorems

McAllester, D. A. (1999) · 1999

Earlier work this paper cites.

Majorizing measures without measures

Talagrand, M. (2001) · 2001

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Combining pac-bayesian and generic chaining bounds

Audibert, J.-Y. and Bousquet, O. (2007) · 2007

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Pac-bayesian supervised classification: the thermodynamics of statistical learning

Original

Catoni, O. (2007) · 2007

Earlier work this paper cites.

Optimal transport: old and new

Villani, C. (2008) · 2008

Earlier work this paper cites.

Bayesian learning via stochastic gradient langevin dynamics

Welling, M. and Teh, Y. W. (2011) · 2011

Cited alongside, same era.

Pac-bayesian inequalities for martingales

Seldin, Y., Laviolette, F., Cesa-Bianchi, N., Shawe-Taylor, J., and Auer, P. (2012) · 2012

Cited alongside, same era.

Concentration inequalities: A nonasymptotic theory of independence

Boucheron, S., Lugosi, G., and Massart, P. (2013) · 2013

Cited alongside, same era.

Pac-bayes-empirical-bernstein inequality

Tolstikhin, I. O. and Seldin, Y. (2013) · 2013

Cited alongside, same era.

Adding gradient noise improves learning for very deep networks

Original

Neelakantan, A., Vilnis, L., Le, Q. V., Sutskever, I., Kaiser, L., Kurach, K., and Martens, J. (2015) · 2015

Cited alongside, same era.

How much does your data exploration overfit? controlling bias via information usage

Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data

Dziugaite, G. K. and Roy, D. M. (2017) · 2017

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Nearly-tight vc-dimension bounds for piecewise linear neural networks

Harvey, N., Liaw, C., and Mehrabian, A. (2017) · 2017

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Entropy-sgd optimizes the prior of a pac-bayes bound: Generalization properties of entropy-sgd and data-dependent priors

Dziugaite, G. K. and Roy, D. M. (2018) · 2018

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Generalization bounds of SGLD for non-convex learning: Two theoretical viewpoints

Mou, W., Wang, L., Zhai, X., and Zheng, K. (2018) · 2018

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A PAC-bayesian approach to spectrally-normalized margin bounds for neural networks

Neyshabur, B., Bhojanapalli, S., and Srebro, N. (2018) · 2018

Later among the works it cites.