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Learning-to-optimize leverages machine learning to accelerate optimization algorithms.
Some methods of speeding up the convergence of iteration methods
Boris T. Polyak · 1964
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Semianalytic and subanalytic sets
Edward Bierstone and Pierre D. Milman · 1988
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On gradients of functions definable in o-minimal structures
Krzysztof Kurdyka · 1998
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Variational Analysis
R. T. Rockafellar and R. J.-B. Wets · 1998
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(Not) Bounding the True Error
John Langford and Rich Caruana · 2001
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PAC-Bayes & Margins
John Langford and John Shawe-Taylor · 2002
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PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
Matthias Seeger · 2002
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Statistical Learning Theory and Stochastic Optimization
Olivier Catoni · 2004
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Convergence of the Iterates of Descent Methods for Analytic Cost Functions
Pierre-Antoine Absil, Robert Mahony, and Ben Andrews · 2005
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PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning
Olivier Catoni · 2007
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On the convergence of the proximal algorithm for nonsmooth functions involving analytic features
Hedy Attouch and Jérôme Bolte · 2009
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PAC-Bayesian Learning of Linear Classifiers
Pascal Germain, Alexandre Lacasse, François Laviolette, and Mario Marchand · 2009
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Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
Hédy Attouch, Jérôme Bolte, Patrick Redont, and Antoine Soubeyran · 2010
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Learning Fast Approximations of Sparse Coding
Karol Gregor and Yann LeCun · 2010
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PAC-Bayes Bounds with Data Dependent Priors
Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, and Shiliang Sun · 2012
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Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods
Hedy Attouch, Jérôme Bolte, and Benar Fux Svaiter · 2013
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Tighter PAC-Bayes bounds through distribution-dependent priors
Guy Lever, François Laviolette, and John Shawe-Taylor · 2013
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Proximal alternating linearized minimization for nonconvex and nonsmooth problems
Jérôme Bolte, Shoham Sabach, and Marc Teboulle · 2014
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Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees
Jean Honorio and Tommi Jaakkola · 2014
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iPiano: Inertial Proximal Algorithm for Nonconvex Optimization
Peter Ochs, Yunjin Chen, Thomas Brox, and Thomas Pock · 2014
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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PAC-Bayesian Bounds based on the Rényi Divergence
Luc Bégin, Pascal Germain, François Laviolette, and Jean-Francis Roy · 2016
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Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation
Suhas Sreehari, S. V. Venkatakrishnan, Brendt Wohlberg, Gregery T. Buzzard, Lawrence F. Drummy, Jeffrey P. Simmons, and Charles A. Bouman · 2016
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Maximal Sparsity with Deep Networks?
Bo Xin, Yizhou Wang, Wen Gao, David Wipf, and Baoyuan Wang · 2016
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Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications
Stanley H. Chan, Xiran Wang, and Omar A. Elgendy · 2017
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Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data
Gintare Karolina Dziugaite and Daniel M. Roy · 2017
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Image Restoration by Iterative Denoising and Backward Projections
Tom Tirer and Raja Giryes · 2019
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Understanding Deep Architecture with Reasoning Layer
Xinshi Chen, Yufei Zhang, Christoph Reisinger, and Le Song · 2020
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Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Regev Cohen, Michael Elad, and Peyman Milanfar · 2021
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On the Role of Data in PAC-Bayes Bounds
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, 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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Ben London · 2017
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Scene-Adapted plug-and-play algorithm with convergence guarantees
Afonso M. Teodoro, José M. Bioucas-Dias, and Mário A. T. Figueiredo · 2017
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A Strongly Quasiconvex PAC-Bayesian Bound
Niklas Thiemann, Christian Igel, Olivier Wintenberger, and Yevgeny Seldin · 2017
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Learned Optimizers that Scale and Generalize
Olga Wichrowska, Niru Maheswaranathan, Matthew W. Hoffman, Sergio Gomez Colmenarejo, Misha Denil, Nando Freitas, and Jascha Sohl-Dickstein · 2017
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Simpler PAC-Bayesian bounds for hostile data
Pierre Alquier and Benjamin Guedj · 2018
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Plug-and-play Unplugged: Optimization-Free Reconstruction Using Consensus Equilibrium
Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari, and Charles A. Bouman · 2018
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Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
Xiaohan Chen, Jialin Liu, Zhangyang Wang, and Wotao Yin · 2018
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Enhanced Convergent PNP Algorithms For Image Restoration
Matthieu Terris, Audrey Repetti, Jean-Christophe Pesquet, and Yves Wiaux · 2021
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Integral Probability Metrics PAC-Bayes Bounds
Ron Amit, Baruch Epstein, Shay Moran, and Ron Meir · 2022
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Learning to Optimize: A Primer and A Benchmark
Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, and Wotao Yin · 2022
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Total Deep Variation: A Stable Regularization Method for Inverse Problems
Erich Kobler, Alexander Effland, Karl Kunisch, and Thomas Pock · 2022
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Practical Tradeoffs between Memory, Compute, and Performance in Learned Optimizers
Luke Metz, C. Daniel Freeman, James Harrison, Niru Maheswaranathan, and Jascha Sohl-Dickstein · 2022
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A Simple Guard for Learned Optimizers
Isabeau Prémont-Schwarz, Jaroslav Vítků, and Jan Feyereisl · 2022
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Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche and Benjamin Guedj · 2023
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Safeguarded Learned Convex Optimization
Howard Heaton, Xiaohan Chen, Zhangyang Wang, and Wotao Yin · 2023
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Towards Constituting Mathematical Structures for Learning to Optimize
Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, and HanQin Cai · 2023
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PAC-Bayesian Learning of Optimization Algorithms
Michael Sucker and Peter Ochs · 2023
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User-friendly Introduction to PAC-Bayes Bounds
Pierre Alquier · 2024
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From Learning to Optimize to Learning Optimization Algorithms
Camille Castera and Peter Ochs · 2024
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A Markovian Model for Learning-to-Optimize
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Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, and Maxim Raginsky · 2025
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