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A fundamental question in theoretical machine learning is generalization.
“A primer on PAC-Bayesian learning”
Benjamin Guedj · 1901
Earlier work this paper cites.
“On the Probable Errors of Frequency-Constants”
F.. Edgeworth · 1908
Earlier work this paper cites.
“PAC-Bayes with Backprop”
Omar Rivasplata, Vikram Tankasali and Csaba Szepesvari · 1908
Earlier work this paper cites.
“A modern introduction to online learning”
Francesco Orabona · 1912
Earlier work this paper cites.
“Rademacher penalties and structural risk minimization”
V. Koltchinskii · 1914
Earlier work this paper cites.
“On the mathematical foundations of theoretical statistics”
R.. Fisher and Edward Russell · 1922
Earlier work this paper cites.
“On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples”
William. Thompson · 1933
Earlier work this paper cites.
“A Mathematical Theory of Communication”
Claude Shannon · 1948
Earlier work this paper cites.
“On Conjugate Convex Functions”
W. Fenchel · 1949
Earlier work this paper cites.
“Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary beta-Mixing Processes”
Liva Ralaivola, Marie Szafranski and Guillaume Stempfel · 1956
Earlier work this paper cites.
“On tables of random numbers”
A.. Kolmogorov · 1963
Earlier work this paper cites.
“A formal theory of inductive inference. Part I”
R.J. Solomonoff · 1964
Earlier work this paper cites.
“Convex analysis”, Princeton Mathematical Series
R. Rockafellar · 1970
Earlier work this paper cites.
“On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities”
V. Vapnik and A. Chervonenkis · 1971
Earlier work this paper cites.
“A Theory of the Learnable”
L.. Valiant · 1972
Earlier work this paper cites.
“A new look at the statistical model identification”
H. Akaike · 1974
Earlier work this paper cites.
“Theory of Pattern Recognition [in Russian]”
V. Vapnik and A. Chervonenkis · 1974
Earlier work this paper cites.
“ I I -Divergence Geometry of Probability Distributions and Minimization Problems”
I. Csiszar · 1975
Earlier work this paper cites.
“Asymptotic evaluation of certain Markov process expectations for large time, I”
M.. Donsker and S… Varadhan · 1975
Earlier work this paper cites.
“Estimation des densités : risque minimax”
Jean Bretagnolle and Catherine Huber · 1978
Earlier work this paper cites.
“Modeling by shortest data description”
Jorma Rissanen · 1978
Earlier work this paper cites.
“A Finite Sample Distribution-Free Performance Bound for Local Discrimination Rules”
W.. Rogers and T.. Wagner · 1978
Earlier work this paper cites.
“Estimating the Dimension of a Model”
Gideon Schwarz · 1978
Earlier work this paper cites.
“Distribution-free performance bounds for potential function rules”
L. Devroye and T. Wagner · 1979
Earlier work this paper cites.
“The importance of convexity in learning with squared loss”
Wee Lee, P.L. Bartlett and R.C. Williamson · 1980
Earlier work this paper cites.
“A Universal Prior for Integers and Estimation by Minimum Description Length”
Jorma Rissanen · 1983
Earlier work this paper cites.
“Some Limit Theorems for Empirical Processes”
Evarist Gine and Joel Zinn · 1984
Earlier work this paper cites.
“Occam’s Razor”
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler and Manfred. Warmuth · 1987
Earlier work this paper cites.
“Real and Complex Analysis, 3rd Ed.”
Walter Rudin · 1987
Earlier work this paper cites.
“Learnability and the Vapnik-Chervonenkis Dimension”
Anselm Blumer, A. Ehrenfeucht, David Haussler and Manfred. Warmuth · 1989
Earlier work this paper cites.
“Minimum complexity density estimation”
A.R. Barron and T.M. Cover · 1991
Earlier work this paper cites.
“Predicting (0, 1)-functions on randomly drawn points”
D. Haussler, N. Littlestone and M.K. Warmuth · 1994
Earlier work this paper cites.
“Bayesian Learning for Neural Networks”, 1994
Radford. Neal · 1994
Earlier work this paper cites.
“A Measure Concentration Inequality for Contracting Markov Chains”
K. Marton · 1996
Earlier work this paper cites.
“Multitask Learning”
R. Caruana · 1997
Earlier work this paper cites.
“A PAC Analysis of a Bayesian Estimator”
John Shawe-Taylor and Robert. Williamson · 1997
Earlier work this paper cites.
“The minimum description length principle in coding and modeling”
A. Barron, J. Rissanen and Bin Yu · 1998
Earlier work this paper cites.
“Some PAC-Bayesian Theorems”
David. McAllester · 1998
Earlier work this paper cites.
“A metric for distributions with applications to image databases”
Y. Rubner, C. Tomasi and L.J. Guibas · 1998
Earlier work this paper cites.
“Learning to Learn: Introduction and Overview”
S. Thrun and L. Pratt · 1998
Earlier work this paper cites.
“Estimation of Mixture Models”, 1999
Qiang Li · 1999
Earlier work this paper cites.
“PAC-Bayesian Model Averaging”
David. McAllester · 1999
Earlier work this paper cites.
“Margin Distribution Bounds on Generalization”
John Shawe-Taylor and Nello Cristianini · 1999
Earlier work this paper cites.
“The information bottleneck method”
Naftali Tishby, Fernando. Pereira and William Bialek · 1999
Earlier work this paper cites.
“Information-theoretic determination of minimax rates of convergence”
Yuhong Yang and Andrew Barron · 1999
Earlier work this paper cites.
“A Model of Inductive Bias Learning”
J. Baxter · 2000
Earlier work this paper cites.
“An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”
Nello Cristianini and John Shawe-Taylor · 2000
Earlier work this paper cites.
“Rademacher Processes and Bounding the Risk of Function Learning”
Vladimir Koltchinskii and Dmitriy Panchenko · 2000
Earlier work this paper cites.
“Some Remarks on the Value-at-Risk and the Conditional Value-at-Risk”
Georg. Pflug · 2000
Earlier work this paper cites.
“Concentration of Measure Inequalities for Markov Chains and Φ \Phi -Mixing Processes”
Paul-Marie Samson · 2000
Earlier work this paper cites.
“Rademacher and Gaussian Complexities: Risk Bounds and Structural Results”
Peter. Bartlett and Shahar Mendelson · 2001
Earlier work this paper cites.
“(Not) Bounding the True Error”
John Langford and Rich Caruana · 2001
Earlier work this paper cites.
“Bounds for Averaging Classifiers”
John Langford and Matthias Seeger · 2001
Earlier work this paper cites.
“Rademacher and Gaussian Complexities: Risk Bounds and Structural Results”
Peter. Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
“Stability and Generalization”
Olivier Bousquet and André Elisseeff · 2002
Earlier work this paper cites.
“A PAC-Bayesian margin bound for linear classifiers”
R. Herbrich and T. Graepel · 2002
Earlier work this paper cites.
“Almost-Everywhere Algorithmic Stability and Generalization Error”
Samuel Kutin and Partha Niyogi · 2002
Earlier work this paper cites.
“Quantitatively Tight Sample Complexity Bounds”, 2002
John Langford · 2002
Earlier work this paper cites.
“PAC-Bayes & margins”
John Langford and John Shawe-Taylor · 2002
Earlier work this paper cites.
“PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification”
M. Seeger · 2002
Earlier work this paper cites.
“Relating Data Compression and Learnability”
Nick Littlestone and Manfred. Warmuth · 2003
Earlier work this paper cites.
“PAC-Bayesian Stochastic Model Selection”
David. McAllester · 2003
Earlier work this paper cites.
“Simplified PAC-Bayesian margin bounds”
David. McAllester · 2003
Earlier work this paper cites.
“A better variance control for PAC-Bayesian classification”, 2004
Jean-Yves Audibert · 2004
Earlier work this paper cites.
“A PAC-Bayesian approach to adaptive classification”, 2004
Olivier Catoni · 2004
Earlier work this paper cites.
“Statistical Learning Theory and Stochastic Optimization”, Lecture Notes in Mathematics: Saint-Flour Summer School on Probability Theory XXXI 2001, 2004
Olivier Catoni · 2004
Earlier work this paper cites.
“A Note on the PAC Bayesian Theorem”
Andreas Maurer · 2004
Earlier work this paper cites.
“A survey on domain adaptation theory: learning bounds and theoretical guarantees”
Ievgen Redko et al · 2004
Earlier work this paper cites.
“Stability results in learning theory”
Alexander Rakhlin, Sayan Mukherjee and Tomaso Poggio · 2005
Earlier work this paper cites.
“Information-theoretic upper and lower bounds for statistical estimation”
Tong Zhang · 2005
Earlier work this paper cites.
“Transductive and inductive adaptative inference for regression and density estimation”, 2006
Pierre Alquier · 2006
Earlier work this paper cites.
“Tighter PAC-Bayes Bounds.”
Amiran Ambroladze, Emilio Parrado-Hernandez and John Shawe-Taylor · 2006
Earlier work this paper cites.
“On Bayesian Bounds”
Arindam Banerjee · 2006
Earlier work this paper cites.
“Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)”
Thomas. Cover and Joy. Thomas · 2006
Earlier work this paper cites.
“PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier”
Alexandre Lacasse et al · 2006
Earlier work this paper cites.
“Information Theory and Mixing Least-Squares Regressions”
G. Leung and A.R. Barron · 2006
Earlier work this paper cites.
“Combining PAC-Bayesian and Generic Chaining Bounds”
Jean-Yves Audibert and Olivier Bousquet · 2007
Earlier work this paper cites.
“PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning”
O. Catoni · 2007
Earlier work this paper cites.
“Aggregation by exponential weighting and sharp oracle inequalities”
Arnak. Dalalyan and Alexandre. Tsybakov · 2007
Earlier work this paper cites.
“The minimum description length principle”
Peter Grünwald · 2007
Earlier work this paper cites.
“Concentration inequalities and model selection”, Lecture Notes in Mathematics: Saint-Flour Summer School on Probability Theory XXXIII 2003, 2007
Pascal Massart · 2007
Earlier work this paper cites.
“Lautum Information”
Daniel. Palomar and Sergio Verdu · 2007
Earlier work this paper cites.
“PAC-Bayesian bounds for randomized empirical risk minimizers”
Pierre Alquier · 2008
Earlier work this paper cites.
“Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity”
Arnak. Dalalyan and Alexandre. Tsybakov · 2008
Earlier work this paper cites.
“Concentration Inequalities for Dependent Random Variables via the Martingale Method”
Aryeh Kontorovich and Kavita Ramanan · 2008
Earlier work this paper cites.
“Optimal transport – Old and new” 338
Cédric Villani · 2008
Earlier work this paper cites.
“PAC-Bayesian Learning of Linear Classifiers”
Pascal Germain, Alexandre Lacasse, François Laviolette and Mario Marchand · 2009
Earlier work this paper cites.
“From PAC-Bayes Bounds to KL Regularization”
Pascal Germain et al · 2009
Earlier work this paper cites.
“PAC-Bayes Analysis Of Maximum Entropy Classification”
John Shawe-Taylor and David Hardoon · 2009
Earlier work this paper cites.
“PAC-Bayesian Model Selection for Reinforcement Learning”
Mahdi Fard and Joelle Pineau · 2010
Earlier work this paper cites.
“A PAC-Bayes Bound for Tailored Density Estimation”
Matthew Higgs and John Shawe-Taylor · 2010
Earlier work this paper cites.
“Distribution-Dependent PAC-Bayes Priors”
Guy Lever, Francois Laviolette and John Shawe-Taylor · 2010
Earlier work this paper cites.
“Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization”
XuanLong Nguyen, Martin. Wainwright and Michael. Jordan · 2010
Earlier work this paper cites.
“PAC-Bayesian Analysis of Co-clustering and Beyond”
Yevgeny Seldin and Naftali Tishby · 2010
Earlier work this paper cites.
“Learnability, Stability and Uniform Convergence”
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro and Karthik Sridharan · 2010
Earlier work this paper cites.
“PAC-Bayesian bounds for sparse regression estimation with exponential weights”
Pierre Alquier and Karim Lounici · 2011
Earlier work this paper cites.
“Information Theory: Coding Theorems for Discrete Memoryless Systems”
I. Csiszar and J. Körner · 2011
Earlier work this paper cites.
“Sparse regression learning by aggregation and Langevin Monte-Carlo”
Arnak. Dalalyan and Alexandre. Tsybakov · 2011
Earlier work this paper cites.
“Optimal aggregation of affine estimators”
Joseph Salmon and Arnak Dalalyan · 2011
Earlier work this paper cites.
“PAC-Bayesian Analysis of Contextual Bandits”
Yevgeny Seldin et al · 2011
Earlier work this paper cites.
“Sharp oracle inequalities for aggregation of affine estimators”
Arnak. Dalalyan and Joseph Salmon · 2012
Earlier work this paper cites.
“Tighter PAC-Bayes bounds through distribution-dependent priors”
Guy Lever, François Laviolette and John Shawe-Taylor · 2012
Earlier work this paper cites.
“PAC-Bayes Bounds with Data Dependent Priors”
Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor and Shiliang Sun · 2012
Earlier work this paper cites.
“Sparse Estimation by Exponential Weighting”
Philippe Rigollet and Alexandre. Tsybakov · 2012
Earlier work this paper cites.
“Tighter Variational Representations of F-Divergences via Restriction to Probability Measures”
Avraham Ruderman, Mark. Reid, Dario Garcia-Garcia and James Petterson · 2012
Earlier work this paper cites.
“PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits”
Yevgeny Seldin et al · 2012
Earlier work this paper cites.
“PAC-Bayesian Inequalities for Martingales”
Yevgeny Seldin et al · 2012
Earlier work this paper cites.
“Active Learning”, Synthesis Lectures on Artificial Intelligence and Machine Learning
Burr Settles · 2012
Earlier work this paper cites.
“Sparse single-index model”
Pierre Alquier and Gérard Biau · 2013
Earlier work this paper cites.
“Concentration inequalities. A nonasymptotic theory of independence”
S. Boucheron, G. Lugosi and P. Massart · 2013
Earlier work this paper cites.
“PAC-Bayesian estimation and prediction in sparse additive models”
Benjamin Guedj and Pierre Alquier · 2013
Earlier work this paper cites.
“General Oracle Inequalities for Gibbs Posterior with Application to Ranking”
Cheng Li, Wenxin Jiang and Martin Tanner · 2013
Earlier work this paper cites.
“A PAC-Bayesian Tutorial with a Dropout Bound”
David. McAllester · 2013
Earlier work this paper cites.
“Concentration of Measure Inequalities in Information Theory, Communications, and Coding”
Maxim Raginsky and Igal Sason · 2013
Earlier work this paper cites.
“PAC-Bayes-Empirical-Bernstein Inequality”
Ilya. Tolstikhin and Yevgeny Seldin · 2013
Earlier work this paper cites.
“PAC-Bayesian Theory for Transductive Learning”
Luc Bégin, Pascal Germain, François Laviolette and Jean-Francis Roy · 2014
Earlier work this paper cites.
“PAC-Bayesian Collective Stability”
Ben London, Bert Huang, Ben Taskar and Lise Getoor · 2014
Earlier work this paper cites.
“Learning without concentration”
Shahar Mendelson · 2014
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“A PAC-Bayesian bound for Lifelong Learning”
A. Pentina and C. Lampert · 2014
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“Understanding Machine Learning: From Theory to Algorithms”
Shai Shalev-Shwartz and Shai Ben-David · 2014
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“Rényi divergence and Kullback-Leibler divergence”
Tim Van Erven and Peter Harremoës · 2014
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“Generalization in Adaptive Data Analysis and Holdout Reuse”
Cynthia Dwork et al · 2015
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“Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm”
Pascal Germain et al · 2015
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“Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability”
A. Farid and A. Majumdar · 2021
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“How Tight Can PAC-Bayes be in the Small Data Regime?”
Andrew.. Foong, Wessel. Bruinsma, David. Burt and Richard. Turner · 2021
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“Sharpness-aware Minimization for Efficiently Improving Generalization”
Pierre Foret, Ariel Kleiner, Hossein Mobahi and Behnam Neyshabur · 2021
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“On Information Plane Analyses of Neural Network Classifiers—–A Review”
Bernhard. Geiger · 2021
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“PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes”
P. Grünwald, T. Steinke and L. Zakynthinou · 2021
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“PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses”
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“Norm-Based Capacity Control in Neural Networks”
Behnam Neyshabur, Ryota Tomioka and Nathan Srebro · 2015
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“Fast rates in statistical and online learning”
Tim Van Erven et al · 2015
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“ α \alpha -Mutual Information”
Sergio Verdú · 2015
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“Weak transport inequalities and applications to exponential and oracle inequalities”
Olivier Wintenberger · 2015
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“On the properties of variational approximations of Gibbs posteriors”
Pierre Alquier, James Ridgway and Nicolas Chopin · 2016
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“Algorithmic Stability for Adaptive Data Analysis”
Raef Bassily et al · 2016
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Maxime Haddouche, Benjamin Guedj, Omar Rivasplata and John Shawe-Taylor · 2021
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“Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning”
Hassan Hafez-Kolahi, Behrad Moniri, Shohreh Kasaei and Mahdieh Baghshah · 2021
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“Towards a Unified Information-Theoretic Framework for Generalization”
Mahdi Haghifam, Gintare Dziugaite, Shay Moran and Daniel. Roy · 2021
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“Information-theoretic generalization bounds for black-box learning algorithms”
Hrayr Harutyunyan, Maxim Raginsky, Greg Steeg and Aram Galstyan · 2021
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“Data-dependent PAC-Bayesian bounds in the random-subset setting with applications to neural networks”
F. Hellström and G. Durisi · 2021
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“Fast-Rate Loss Bounds via Conditional Information Measures with Applications to Neural Networks”
F. Hellström and G. Durisi · 2021
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“Information-Theoretic Generalization Bounds for Meta-Learning and Applications”
S.. Jose and O. Simeone · 2021
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“Transfer Meta-Learning: Information- Theoretic Bounds and Information Meta-Risk Minimization”
S.. Jose, O. Simeone and G. Durisi · 2021
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“A Unified PAC-Bayesian Framework for Machine Unlearning via Information Risk Minimization”
S.. Jose and Osvaldo Simeone · 2021
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“An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning”
S.. Jose and Osvaldo Simeone · 2021
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“Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence”
S.. Jose and Osvaldo Simeone · 2021
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“Advances and Open Problems in Federated Learning”
Peter Kairouz et al · 2021
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“A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks”
Renjie Liao, Raquel Urtasun and Richard Zemel · 2021
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“Towards Out-Of-Distribution Generalization: A Survey”
Jiashuo Liu et al · 2021
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“Statistical Generalization Performance Guarantee for Meta-Learning with Data Dependent Prior”
Tianyu Liu, Jie Lu, Zheng Yan and Guangquan Zhang · 2021
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“Meta-Strategy for Learning Tuning Parameters with Guarantees”
Dimitri Meunier and Pierre Alquier · 2021
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“Rényi Divergence Based Bounds on Generalization Error”
Eeshan Modak, Himanshu Asnani and Vinod. Prabhakaran · 2021
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“Information-Theoretic Generalization Bounds for Stochastic Gradient Descent”
Gergely Neu, Gintare Dziugaite, Mahdi Haghifam and Daniel. Roy · 2021
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“Novel Change of Measure Inequalities with Applications to PAC-Bayesian Bounds and Monte Carlo Estimation”
Yuki Ohnishi and Jean Honorio · 2021
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“Tighter Risk Certificates for Neural Networks”
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor and Csaba Szepesvári · 2021
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“Information-Theoretic Stability and Generalization”
Maxim Raginsky, Alexander Rakhlin and Aolin Xu · 2021
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“Conditional Mutual Information-Based Generalization Bound for Meta Learning”
A. Rezazadeh, Sharu. Jose, G. Durisi and O. Simeone · 2021
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“Upper Bounds on the Generalization Error of Private Algorithms for Discrete Data”
Borja Rodríguez-Gálvez, Germán Bassi and Mikael Skoglund · 2021
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“On Random Subset Generalization Error Bounds and the Stochastic Gradient Langevin Dynamics Algorithm”
Borja Rodríguez-Gálvez, Germán Bassi, Ragnar Thobaben and Mikael Skoglund · 2021
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“Tighter expected generalization error bounds via Wasserstein distance”
Borja Rodríguez-Gálvez, Germán Bassi, Ragnar Thobaben and Mikael Skoglund · 2021
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“PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees”
J. Rothfuss, V. Fortuin, M. Josifoski and A. Krause · 2021
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“A PAC-Bayes Analysis of Adversarial Robustness”
Paul Viallard, Eric Vidot, Amaury Habrard and Emilie Morvant · 2021
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“Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD”
Bohan Wang et al · 2021
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“Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels”
Hao Wang, Yizhe Huang, Rui Gao and Flavio Calmon · 2021
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“Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound”
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“Understanding Deep Learning (Still) Requires Rethinking Generalization”
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“An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift”
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“Tighter Expected Generalization Error Bounds via Convexity of Information Measures”
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“Integral Probability Metrics PAC-Bayes Bounds”
Ron Amit, Baruch Epstein, Shay Moran and Ron Meir · 2022
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“Stability Based Generalization Bounds for Exponential Family Langevin Dynamics”
Arindam Banerjee, Tiancong Chen, Xinyan Li and Yingxue Zhou · 2022
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“Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning”
L.. Barnes, A. Dytso and Ha Poor · 2022
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“Non-Vacuous Generalisation Bounds for Shallow Neural Networks”
Felix Biggs and Benjamin Guedj · 2022
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“On Margins and Derandomisation in PAC-Bayes”
Felix Biggs and Benjamin Guedj · 2022
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“On Margins and Generalisation for Voting Classifiers”
Felix Biggs, Valentina Zantedeschi and Benjamin Guedj · 2022
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“Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm”
Yuheng Bu et al · 2022
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“A short note on an inequality between KL and TV”
Clément. Canonne · 2022
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“On PAC-Bayesian reconstruction guarantees for VAEs”
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“Conditionally Gaussian PAC-Bayes”
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“Chained generalisation bounds”
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“From Generalisation Error to Transportation-cost Inequalities and Back”
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“PAC-Bayesian Lifelong Learning for Multi-Armed Bandits”
H. Flynn, D. Reeb, M. Kandemir and J. Peters · 2022
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“Stochastic Training is Not Necessary for Generalization”
Jonas Geiping et al · 2022
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“An Information-Theoretic Analysis of Bayesian Reinforcement Learning”
Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias. Oechtering and Mikael Skoglund · 2022
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“Online PAC-Bayes Learning”
Maxime Haddouche and Benjamin Guedj · 2022
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“Understanding Generalization via Leave-One-Out Conditional Mutual Information”
Mahdi Haghifam, Shay Moran, Daniel. Roy and Gintare Dziugiate · 2022
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“Formal limitations of sample-wise information-theoretic generalization bounds”
Hrayr Harutyunyan, Greg Steeg and Aram Galstyan · 2022
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“Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning”
Haiyun He, Hanshu Yan and Vincent.. Tan · 2022
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“A New Family of Generalization Bounds Using Samplewise Evaluated CMI”
Fredrik Hellström and Giuseppe Durisi · 2022
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“Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness”
Fredrik Hellström and Giuseppe Durisi · 2022
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“Weight Expansion: A New Perspective on Dropout and Generalization”
Gaojie Jin et al · 2022
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“Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning”
S.. Jose, Sangwoo Park and Osvaldo Simeone · 2022
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“PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization”
Sanae Lotfi et al · 2022
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“Generalization Bounds via Convex Analysis”
Gábor Lugosi and Gergely Neu · 2022
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“Probabilistic Machine Learning: An introduction”
Kevin. Murphy · 2022
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“Lecture Notes On Information Theory”
Y. Polyanskiy and Y. Wu · 2022
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“Finite Littlestone Dimension Implies Finite Information Complexity”
A. Pradeep, I. Nachum and M. Gastpar · 2022
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“On Leave-One-Out Conditional Mutual Information For Generalization”
Mohamad Rammal et al · 2022
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“A Unified View on PAC-Bayes Bounds for Meta-Learning”
Arezou Rezazadeh · 2022
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“Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning”
Milad Sefidgaran, Romain Chor and Abdellatif Zaidi · 2022
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“Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms”
Milad Sefidgaran, Amin Gohari, Gaël Richard and Umut Simsekli · 2022
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“Stability-based PAC-Bayes analysis for multi-view learning algorithms”
Shiliang Sun, Mengran Yu, John Shawe-Taylor and Liang Mao · 2022
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“Risk bounds for aggregated shallow neural networks using Gaussian priors”
Laura Tinsi and Arnak Dalalyan · 2022
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“PAC-Bayes Information Bottleneck”
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