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Bounding the generalization error of a supervised learning algorithm is one of the most important problems in learning theory, and various approaches have been developed.
Elementary principles of statistical mechanics
Gibbs, J. W · 1902
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An invariant form for the prior probability in estimation problems
Jeffreys, H · 1946
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Diffusion for global optimization in rˆn
Chiang, T.-S., Hwang, C.-R., and Sheu, S. J · 1987
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An overview of statistical learning theory
Vapnik, V. N · 1999
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On the trend to equilibrium for the fokker-planck equation: an interplay between physics and functional analysis
Markowich, P. A. and Villani, C · 2000
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Stability and generalization
Bousquet, O. and Elisseeff, A · 2002
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Pac-bayesian stochastic model selection
McAllester, D. A · 2003
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Information-theoretic upper and lower bounds for statistical estimation
Zhang, T · 2006
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From ϵ \epsilon -entropy to kl-entropy: Analysis of minimum information complexity density estimation
Zhang, T. et al · 2006
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Mechanism design via differential privacy
McSherry, F. and Talwar, K · 2007
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Conjugate bayesian analysis of the gaussian distribution
Murphy, K. P · 2007
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Lautum information
Palomar, D. P. and Verdú, S · 2008
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Neural network learning: Theoretical foundations
Anthony, M. and Bartlett, P. L · 2009
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Robustness and generalization
Xu, H. and Mannor, S · 2012
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Capacity of diffusion-based molecular communication networks over lti-poisson channels
Aminian, G., Arjmandi, H., Gohari, A., Nasiri-Kenari, M., and Mitra, U · 2015
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On the properties of variational approximations of gibbs posteriors
Alquier, P., Ridgway, J., and Chopin, N · 2016
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Information-theoretic analysis of stability and bias of learning algorithms
Raginsky, M., Rakhlin, A., Tsao, M., Wu, Y., and Xu, A · 2016
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Understanding deep learning requires rethinking generalization
Strengthened information-theoretic bounds on the generalization error
Issa, I., Esposito, A. R., and Gastpar, M · 2019
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Distribution-dependent analysis of gibbs-erm principle
Kuzborskij, I., Cesa-Bianchi, N., and Szepesvári, C · 2019
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How much does your data exploration overfit? controlling bias via information usage
Russo, D. and Zou, J · 2019
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An information-theoretic view of generalization via wasserstein distance
Wang, H., Diaz, M., Santos Filho, J. C. S., and Calmon, F. P · 2019
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Jensen-shannon information based characterization of the generalization error of learning algorithms
Aminian, G., Toni, L., and Rodrigues, M. R · 2020
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Chaining meets chain rule: Multilevel entropic regularization and training of neural networks
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Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2016
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Dependence measures bounding the exploration bias for general measurements
Jiao, J., Han, Y., and Weissman, T · 2017
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Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
Raginsky, M., Rakhlin, A., and Telgarsky, M · 2017
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Information-theoretic analysis of generalization capability of learning algorithms
Xu, A. and Raginsky, M · 2017
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Chaining mutual information and tightening generalization bounds
Asadi, A., Abbe, E., and Verdú, S · 2018
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Generalization error bounds using wasserstein distances
Lopez, A. T. and Jog, V · 2018
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Generalization error bounds via α \alpha -réyni, f f -divergences and maximal leakage
Esposito, A. R., Gastpar, M., and Issa, I · 2019
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Asadi, A. R. and Abbe, E · 2020
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Conditioning and processing: Techniques to improve information-theoretic generalization bounds
Hafez-Kolahi, H., Golgooni, Z., Kasaei, S., and Soleymani, M · 2020
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Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms
Haghifam, M., Negrea, J., Khisti, A., Roy, D. M., and Dziugaite, G. K · 2020
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Reasoning about generalization via conditional mutual information
Steinke, T. and Zakynthinou, L · 2020
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Learning under distribution mismatch and model misspecification
Masiha, M. S., Gohari, A., Yassaee, M. H., and Aref, M. R · 2021
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Information-Theoretic Methods in Data Science
Rodrigues, M. R. and Eldar, Y. C · 2021
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Tighter expected generalization error bounds via wasserstein distance
Rodríguez-Gálvez, B., Bassi, G., Thobaben, R., and Skoglund, M · 2021
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