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Various differentially private algorithms instantiate the exponential mechanism, and require sampling from the distribution $\exp(-f)$ for a suitable function $f$.
Is there an analog of nesterov acceleration for MCMC?
Yi-An Ma, Niladri S. Chatterji, Xiang Cheng, Nicolas Flammarion, Peter L. Bartlett, and Michael I. Jordan · 1902
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Diffusions hypercontractives
Dominique Bakry and Michel Émery · 1985
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Sampling and integration of near log-concave functions
David Applegate and Ravi Kannan · 1991
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Exponential convergence of Langevin distributions and their discrete approximations
Gareth O. Roberts and Richard L. Tweedie · 1996
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General state space markov chains and MCMC algorithms
Gareth O. Roberts and Jeffrey S. Rosenthal · 2004
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Fast algorithms for logconcave functions: Sampling, rounding, integration and optimization
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The geometry of logconcave functions and sampling algorithms
László Lovász and Santosh Vempala · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Private coresets
Dan Feldman, Amos Fiat, Haim Kaplan, and Kobbi Nissim · 2009
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2009
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Differentially private combinatorial optimization
Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, and Kunal Talwar · 2010
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Jordan Awan, Ana Kenney, Matthew Reimherr, and Aleksandra Slavković · 2019
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High-dimensional bayesian inference via the unadjusted Langevin algorithm
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Arun Ganesh and Kunal Talwar · 2019
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Bai Li, Changyou Chen, Hao Liu, and Lawrence Carin · 2019
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Matthew Reimherr and Jordan Awan · 2019
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Rapid convergence of the unadjusted Langevin algorithm: Isoperimetry suffices
Santosh Vempala and Andre Wibisono · 2019
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Near instance-optimality in differential privacy
Hilal Asi and John C. Duchi · 2020
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