Fetching the paper…
Reading the bibliography…
The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999) can incorporate knowledge about the learning algorithm and (data) distribution through the use of distribution-dependent priors, yielding tighter generalization bounds on data-dependent posteriors.
“A PAC analysis of a Bayesian estimator”
John Shawe-Taylor and Robert Williamson · 1997
Earlier work this paper cites.
“PAC-Bayesian Model Averaging”
David. McAllester · 1999
Earlier work this paper cites.
“Bounds for Averaging Classifiers”, 2001
John Langford and Matthias Seeger · 2001
Earlier work this paper cites.
“Stability and generalization”
Olivier Bousquet and Andr\’e Elisseeff · 2002
Earlier work this paper cites.
“Quantitatively tight sample complexity bounds”, 2002
John Langford · 2002
Earlier work this paper cites.
“Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise”
J.C. Mattingly, A.M. Stuart and D.J. Higham · 2002
Earlier work this paper cites.
“A note on the PAC-Bayesian theorem”, 2004
Andreas Maurer · 2004
Earlier work this paper cites.
“Differential Privacy”
Cynthia Dwork · 2006
Earlier work this paper cites.
“PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning”, Lecture Notes-Monograph Series
Olivier Catoni · 2007
Earlier work this paper cites.
“Mechanism Design via Differential Privacy”
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
“Differential privacy: A survey of results”
Cynthia Dwork · 2008
Earlier work this paper cites.
“Bayesian learning via stochastic gradient Langevin dynamics”
Max Welling and Yee Teh · 2011
Earlier work this paper cites.
“Private convex empirical risk minimization and high-dimensional regression”
Daniel Kifer, Adam Smith and Abhradeep Thakurta · 2012
Earlier work this paper cites.
“Tighter PAC-Bayes bounds through distribution-dependent priors”
Guy Lever, Francois Laviolette and John Shawe-Taylor · 2012
Earlier work this paper cites.
“PAC-Bayes bounds with data dependent priors”
Emilio Parrado-Hern\’andez, Amiran Ambroladze, John Shawe-Taylor and Shiliang Sun · 2012
Cited alongside, same era.
“Differential privacy: an exploration of the privacy-utility landscape”, 2013
Darakhshan Mir · 2013
Cited alongside, same era.
Raef Bassily, Adam Smith and Abhradeep Thakurta · 2014
Cited alongside, same era.
“Robust and private Bayesian inference”
Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa and Benjamin Rubinstein · 2014
Cited alongside, same era.
“The algorithmic foundations of differential privacy”
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
“Generalization in adaptive data analysis and holdout reuse”
“Spectrally-normalized margin bounds for neural networks”
Peter Bartlett, Dylan Foster and Matus Telgarsky · 2017
Later among the works it cites.
Gintare Dziugaite and Daniel. Roy · 2017
Later among the works it cites.
“A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity”, 2017
Peter Gr\"unwald and Nishant Mehta · 2017
Later among the works it cites.
“A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent”
Ben London · 2017
Later among the works it cites.
“A PAC-Bayesian approach to spectrally-normalized margin bounds for neural networks”
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester and Nathan Srebro · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cynthia Dwork et al · 2015
Cited alongside, same era.
“Preserving statistical validity in adaptive data analysis”
Cynthia Dwork et al · 2015
Cited alongside, same era.
“Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo”
Yu-Xiang Wang, Stephen. Fienberg and Alexander. Smola · 2015
Cited alongside, same era.
“Algorithmic stability for adaptive data analysis”
Raef Bassily et al · 2016
Cited alongside, same era.
“PAC-Bayesian bounds based on the Rényi divergence”
Luc B\’egin, Pascal Germain, Francois Laviolette and Jean-Francis Roy · 2016
Cited alongside, same era.
“PAC-Bayesian Theory Meets Bayesian Inference”
Pascal Germain, Francis Bach, Alexandre Lacoste and Simon Lacoste-Julien · 2016
Cited alongside, same era.
“Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes”, 2016
Peter Gr\"unwald and Nishant Mehta · 2016
Cited alongside, same era.
Later among the works it cites.
“Exploring generalization in deep learning”
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester and Nati Srebro · 2017
Later among the works it cites.
“Differential privacy and generalization: Sharper bounds with applications”
Luca Oneto, Sandro Ridella and Davide Anguita · 2017
Later among the works it cites.
“Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis”
Maxim Raginsky, Alexander Rakhlin and Matus Telgarsky · 2017
Later among the works it cites.
“A Strongly Quasiconvex PAC-Bayesian Bound”
Niklas Thiemann, Christian Igel, Olivier Wintenberger and Yevgeny Seldin · 2017
Later among the works it cites.
“Simpler PAC-Bayesian Bounds for Hostile Data”
Pierre Alquier and Benjamin Guedj · 2018
Closest in time.
“Global Non-convex Optimization with Discretized Diffusions”, 2018
Murat. Erdogdu, Lester Mackey and Ohad Shamir · 2018
Closest in time.
“PAC-Bayes bounds for stable algorithms with instance-dependent priors”
Omar Rivasplata et al · 2018
Closest in time.
“A Bayesian perspective on generalization and stochastic gradient descent”
Samuel Smith and Quoc Le · 2018
Closest in time.