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Sampling logconcave functions arising in statistics and machine learning has been a subject of intensive study.
Application of the logistic function to bio-assay
Joseph Berkson · 1944
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Robust estimation of a location parameter
Peter J. Huber · 1964
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Computational complexity of real functions
Ker-I Ko and Harvey Friedman · 1982
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Systems of microdifferential equations. based on lecture notes by teresa monteiro fernandes translated from the french. with an introduction by jl brylinski
Masaki Kashiwara · 1983
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On the computational complexity of ordinary differential equations
Ker-I Ko · 1983
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The mixing rate of markov chains, an isoperimetric inequality, and computing the volume
László Lovász and Miklós Simonovits · 1990
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On the randomized complexity of volume and diameter
László Lovász and Miklós Simonovits · 1992
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Random walks in a convex body and an improved volume algorithm
László Lovász and Miklós Simonovits · 1993
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Cauchy-kovaleskaya theorem
A.M. Nakhushev · 1994
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Fast algorithms for polynomial interpolation, integration, and differentiation
A Dutt, M Gu, and V Rokhlin · 1996
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Logistic regression: A primer
Fred C Pampel · 2000
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Andrew Y Ng and Michael I Jordan · 2002
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Majorants for formal power series
Joris van der Hoeven · 2003
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Fast algorithms for logconcave functions: Sampling, rounding, integration and optimization
László Lovász and Santosh Vempala · 2006
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Hit-and-run from a corner
László Lovász and Santosh Vempala · 2006
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A first course in the numerical analysis of differential equations
Arieh Iserles · 2009
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Lipschitz continuous ordinary differential equations are polynomial-space complete
Akitoshi Kawamura · 2010
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Polynomial-time computability in analysis: A survey
Ker-I Ko · 2010
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Complexity theory for operators in analysis
Akitoshi Kawamura and Stephen Cook · 2012
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Applied logistic regression
David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant · 2013
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Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression
Francis Bach · 2014
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Input sparsity and hardness for robust subspace approximation
Kenneth L Clarkson and David P Woodruff · 2015
User-friendly guarantees for the langevin monte carlo with inaccurate gradient
Arnak S Dalalyan and Avetik G Karagulyan · 2017
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Yin Tat Lee and Santosh S Vempala · 2017
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Rapid mixing of hamiltonian monte carlo on strongly log-concave distributions
Oren Mangoubi and Aaron Smith · 2017
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The true cost of stochastic gradient langevin dynamics
Tigran Nagapetyan, Andrew B Duncan, Leonard Hasenclever, Sebastian J Vollmer, Lukasz Szpruch, and Konstantinos Zygalakis · 2017
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Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis
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A hitting time analysis of stochastic gradient langevin dynamics
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Sharp convergence rates for langevin dynamics in the nonconvex setting
Xiang Cheng, Niladri S Chatterji, Yasin Abbasi-Yadkori, Peter L Bartlett, and Michael I Jordan · 2018
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Underdamped langevin mcmc: A non-asymptotic analysis
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Convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation
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Dimensionally tight running time bounds for second-order hamiltonian monte carlo
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Robust sparse recovery via m-estimators
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Relative error tensor low rank approximation
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