Fetching the paper…
Reading the bibliography…
We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al.
J. V. Michalowicz, J. M. Nichols, and F. Bucholtz, “Calculation of differential entropy for a mixed Gaussian distribution,” Entropy , vol. 10, no. 3, pp. 200–206, 2008
2008
Earlier work this paper cites.
S. Boucheron, G. Lugosi, and P. Massart, Concentration inequalities: A nonasymptotic theory of independence . Oxford university press, 2013
2013
Earlier work this paper cites.
S. Shalev-Shwartz and S. Ben-David, Understanding machine learning: From theory to algorithms . Cambridge university press, 2014
2014
Earlier work this paper cites.
D. Russo and J. Zou, “Controlling bias in adaptive data analysis using information theory,” in Artificial Intelligence and Statistics , 2016, pp. 1232–1240
2016
Earlier work this paper cites.
A. Xu and M. Raginsky, “Information-theoretic analysis of generalization capability of learning algorithms,” in Advances in Neural Information Processing Systems , 2017, pp. 2524–2533
2017
Earlier work this paper cites.
A. Asadi, E. Abbe, and S. Verdú, “Chaining mutual information and tightening generalization bounds,” in Advances in Neural Information Processing Systems , 2018, pp. 7234–7243
2018
Cited alongside, same era.
I. Issa, A. R. Esposito, and M. Gastpar, “Strengthened information-theoretic bounds on the generalization error,” in 2019 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2019, pp. 582–586
2019
Cited alongside, same era.
J. Negrea, M. Haghifam, G. K. Dziugaite, A. Khisti, and D. M. Roy, “Information-theoretic generalization bounds for SGLD via data-dependent estimates,” in Advances in Neural Information Processing Systems , 2019, pp. 11 015–11 025
2019
Cited alongside, same era.
2020
Cited alongside, same era.
Y. Bu, S. Zou, and V. V. Veeravalli, “Tightening mutual information based bounds on generalization error,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 121–130, 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.