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This work presents several expected generalization error bounds based on the Wasserstein distance.
T. Popoviciu, “Sur les équations algébriques ayant toutes leurs racines réelles,” Mathematica , vol. 9, pp. 129–145, 1935
1935
Earlier work this paper cites.
J. Bretagnolle and C. Huber, “Estimation des densités: risque minimax,” in Séminaire de Probabilités XII . Springer, 1978, pp. 342–363, in French
1978
Earlier work this paper cites.
D. Haussler, “Sphere packing numbers for subsets of the boolean n-cube with bounded vapnik-chervonenkis dimension,” Journal of Combinatorial Theory, Series A , vol. 69, no. 2, pp. 217–232, 1995
1995
Earlier work this paper cites.
O. Bousquet and A. Elisseeff, “Stability and generalization,” Journal of machine learning research , vol. 2, no. Mar, pp. 499–526, 2002
2002
Earlier work this paper cites.
J.-Y. Audibert and O. Bousquet, “Pac-bayesian generic chaining.” in NIPS . Citeseer, 2003, pp. 1125–1132
2003
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of Information Theory , 2nd ed. John Wiley & Sons, 2006
2006
Earlier work this paper cites.
J.-Y. Audibert and O. Bousquert, “Combining pac-bayesian and generic chaining bounds.” Journal of Machine Learning Research , vol. 8, no. 4, 2007
2007
Earlier work this paper cites.
C. Villani, Optimal Transport: Old and New , ser. Grundlehren der mathematischen Wissenschaften. Springer Science & Business Media, 2008, vol. 338
2008
Earlier work this paper cites.
D. P. Palomar and S. Verdú, “Lautum information,” IEEE Transactions on Information Theory , vol. 54, no. 3, pp. 964–975, 2008
2008
Earlier work this paper cites.
I. Steinwart and A. Christmann, Support vector machines . Springer Science & Business Media, 2008
2008
Earlier work this paper cites.
P. Harremoës and I. Vajda, “On pairs of f f -divergences and their joint range,” IEEE Transactions on Information Theory , vol. 57, no. 6, pp. 3230–3235, 2011
2011
Earlier work this paper cites.
R. M. Gray, Source coding theory , ser. Engineering and Computer Science. Springer Science & Business Media, 2012, vol. 83
2012
Earlier work this paper cites.
V. Vapnik, The Nature of Statistical Learning Theory , ser. Information Science and Statistics. Springer Science & Business Media, 2013
2013
Earlier work this paper cites.
J. N. McDonald and N. A. Weiss, A Course in Real Analysis , 2nd ed. Cambridge, Massachusetts: Elsevier, 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.
R. van Handel, “Probability in high dimension,” Princeton University, NJ, Tech. Rep., 2014
2014
Cited alongside, same era.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy.” Foundations and Trends in Theoretical Computer Science , vol. 9, no. 3-4, pp. 211–407, 2014
2014
Cited alongside, same era.
C. Dwork, V. Feldman, M. Hardt, T. Pitassi, O. Reingold, and A. Roth, “Generalization in adaptive data analysis and holdout reuse,” in Advances in Neural Information Processing Systems , 2015, pp. 2350–2358
2015
Cited alongside, same era.
M. Raginsky, A. Rakhlin, M. Tsao, Y. Wu, and A. Xu, “Information-theoretic analysis of stability and bias of learning algorithms,” in 2016 IEEE Information Theory Workshop (ITW) . IEEE, 2016, pp. 26–30
2016
Cited alongside, same era.
M. Bun and T. Steinke, “Concentrated differential privacy: Simplifications, extensions, and lower bounds,” in Theory of Cryptography Conference . Springer, 2016, pp. 635–658
H. Wang, M. Diaz, J. C. S. Santos Filho, and F. P. Calmon, “An information-theoretic view of generalization via wasserstein distance,” in 2019 IEEE International Symposium on Information Theory (ISIT) . IEEE, 2019, pp. 577–581
2019
Later among the works it cites.
B. Guedj, “A primer on PAC-Bayesian learning,” arXiv preprint arXiv:1901.05353 , 2019
2019
Later among the works it cites.
T. Steinke and L. Zakynthinou, “Reasoning about generalization via conditional mutual information,” in Conference on Learning Theory , ser. Proceedings of Machine Learning Research, vol. 125, Jul. 2020, pp. 3437–3452
2020
Later among the works it cites.
D. Russo and J. Zou, “How much does your data exploration overfit? Controlling bias via information usage,” IEEE Transactions on Information Theory , vol. 66, no. 1, pp. 302–323, Jan. 2020
2020
Later among the works it cites.
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2016
Cited alongside, same era.
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
Cited alongside, same era.
Y. Polyanskiy and Y. Wu, “Lecture notes on Information Theory,” MIT (6.441), UIUC (ECE 563), Yale (STAT 664) , 2017
2017
Cited alongside, same era.
Y. Wu, “Lecture notes on information-theoretic methods for high-dimensional statistics,” Lecture Notes for ECE598YW (UIUC) , vol. 16, 2017
2017
Cited alongside, same era.
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.
2018
Cited alongside, same era.
A. T. Lopez and V. Jog, “Generalization error bounds using wasserstein distances,” in 2018 IEEE Information Theory Workshop (ITW) . IEEE, 2018, pp. 1–5
2018
Cited alongside, same era.
B. Gao and L. Pavel, “On the properties of the softmax function with application in game theory and reinforcement learning,” 2018
2018
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, May 2020
2020
Later among the works it cites.
F. Hellström and G. Durisi, “Generalization bounds via information density and conditional information density,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 3, pp. 824–839, Nov. 2020
2020
Later among the works it cites.
B. Rodríguez-Gálvez, G. Bassi, R. Thobaben, and M. Skoglund, “On random subset generalization error bounds and the stochastic gradient langevin dynamics algorithm,” in IEEE Information Theory Workshop (ITW) . IEEE, 2020
2020
Later among the works it cites.
M. Haghifam, J. Negrea, A. Khisti, D. M. Roy, and G. K. Dziugaite, “Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms,” in Advances in Neural Information Processing Systems , 2020
2020
Later among the works it cites.
H. Hafez-Kolahi, Z. Golgooni, S. Kasaei, and M. Soleymani, “Conditioning and processing: Techniques to improve information-theoretic generalization bounds,” Advances in Neural Information Processing Systems , vol. 33, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
Y. Bu, W. Gao, S. Zou, and V. Veeravalli, “Information-theoretic understanding of population risk improvement with model compression,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 04, pp. 3300–3307, Apr. 2020
2020
Later among the works it cites.
O. Bousquet, Y. Klochkov, and N. Zhivotovskiy, “Sharper bounds for uniformly stable algorithms,” in Conference on Learning Theory , ser. Proceedings of Machine Learning Research, 2020, pp. 610–626
2020
Later among the works it cites.
A. R. Esposito, M. Gastpar, and I. Issa, “Generalization error bounds via rényi-, f f -divergences and maximal leakage,” IEEE Transactions on Information Theory , vol. 67, no. 8, pp. 4986–5004, 2021
2021
Closest in time.
B. Rodríguez-Gálvez, G. Bassi, and M. Skoglund, “Upper bounds on the generalization error of private algorithms for discrete data,” IEEE Transactions on Information Theory , vol. 67, no. 11, pp. 7362–7379, 2021
2021
Closest in time.