Fetching the paper…
Reading the bibliography…
We derive information-theoretic generalization bounds for supervised learning algorithms based on the information contained in predictions rather than in the output of the training algorithm.
On the density of families of sets
N. Sauer · 1972
Earlier work this paper cites.
A combinatorial problem; stability and order for models and theories in infinitary languages
S. Shelah · 1972
Earlier work this paper cites.
Recursive stochastic algorithms for global optimization in rˆd
S. B. Gelfand and S. K. Mitter · 1991
Earlier work this paper cites.
Statistical learning theory new york
V. Vapnik · 1998
Earlier work this paper cites.
Stability and generalization
O. Bousquet and A. Elisseeff · 2002
Earlier work this paper cites.
Estimation of entropy and mutual information
L. Paninski · 2003
Earlier work this paper cites.
The information lost in erasures
S. Verdu and T. Weissman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Mnist handwritten digit database
Y. LeCun, C. Cortes, and C. Burges · 2010
Earlier work this paper cites.
Entropy and information theory
R. M. Gray · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
M. Welling and Y. W. Teh · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Algorithmic stability and uniform generalization
I. M. Alabdulmohsin · 2015
Earlier work this paper cites.
Algorithmic stability for adaptive data analysis
R. Bassily, K. Nissim, A. Smith, T. Steinke, U. Stemmer, and J. Ullman · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Information-theoretic analysis of stability and bias of learning algorithms
M. Raginsky, A. Rakhlin, M. Tsao, Y. Wu, and A. Xu · 2016
Cited alongside, same era.
On-average kl-privacy and its equivalence to generalization for max-entropy mechanisms
Y.-X. Wang, J. Lei, and S. E. Fienberg · 2016
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Cited alongside, same era.
Membership inference attacks against machine learning models
Information-theoretic generalization bounds for sgld via data-dependent estimates
J. Negrea, M. Haghifam, G. K. Dziugaite, A. Khisti, and D. M. Roy · 2019
Later among the works it cites.
How much does your data exploration overfit? controlling bias via information usage
D. Russo and J. Zou · 2019
Later among the works it cites.
Towards a unified theory of learning and information
I. Alabdulmohsin · 2020
Later among the works it cites.
Tightening mutual information-based bounds on generalization error
Y. Bu, S. Zou, and V. V. Veeravalli · 2020
Later among the works it cites.
Conditioning and processing: Techniques to improve information-theoretic generalization bounds
H. Hafez-Kolahi, Z. Golgooni, S. Kasaei, and M. Soleymani · 2020
Later among the works it cites.
Sharpened generalization bounds based on conditional mutual information and an application to noisy, iterative algorithms
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Information-theoretic analysis of generalization capability of learning algorithms
A. Xu and M. Raginsky · 2017
Cited alongside, same era.
Learners that use little information
R. Bassily, S. Moran, I. Nachum, J. Shafer, and A. Yehudayoff · 2018
Cited alongside, same era.
Calibrating noise to variance in adaptive data analysis
V. Feldman and T. Steinke · 2018
Cited alongside, same era.
Generalization error bounds for noisy, iterative algorithms
A. Pensia, V. Jog, and P.-L. Loh · 2018
Cited alongside, same era.
High-dimensional probability: An introduction with applications in data science , volume 47
R. Vershynin · 2018
Cited alongside, same era.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
Cited alongside, same era.
M. Haghifam, J. Negrea, A. Khisti, D. M. Roy, and G. K. Dziugaite · 2020
Later among the works it cites.
Fantastic generalization measures and where to find them
Y. Jiang*, B. Neyshabur*, H. Mobahi, D. Krishnan, and S. Bengio · 2020
Later among the works it cites.
Reasoning About Generalization via Conditional Mutual Information
T. Steinke and L. Zakynthinou · 2020
Later among the works it cites.
Generalization error bounds via rényi-, f-divergences and maximal leakage
A. R. Esposito, M. Gastpar, and I. Issa · 2021
Closest in time.
Membership inference attacks on deep regression models for neuroimaging
U. Gupta, D. Stripelis, P. K. Lam, P. Thompson, J. L. Ambite, and G. V. Steeg · 2021
Closest in time.
Estimating informativeness of samples with smooth unique information
H. Harutyunyan, A. Achille, G. Paolini, O. Majumder, A. Ravichandran, R. Bhotika, and S. Soatto · 2021
Closest in time.
Information-theoretic generalization bounds for stochastic gradient descent
G. Neu · 2021
Closest in time.
Information-theoretic stability and generalization
M. Raginsky, A. Rakhlin, and A. Xu · 2021
Closest in time.