Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo
under local conditions for nonconvex optimization

Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization

13 February 2020

Ömer Deniz Akyildiz

Sotirios Sabanis

Papers citing "Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization"

17 / 17 papers shown

Title
On theoretical guarantees and a blessing of dimensionality for nonconvex sampling Martin Chak 35 2 0 12 Nov 2024
Statistical Finite Elements via Interacting Particle Langevin Dynamics Alex Glyn-Davies Connor Duffin Ieva Kazlauskaite Mark Girolami O. Deniz Akyildiz 27 0 0 11 Sep 2024
Kinetic Interacting Particle Langevin Monte Carlo Paul Felix Valsecchi Oliva O. Deniz Akyildiz 18 4 0 08 Jul 2024
Proximal Interacting Particle Langevin Algorithms Paula Cordero Encinar F. R. Crucinio O. Deniz Akyildiz 16 3 0 20 Jun 2024
Subsampling Error in Stochastic Gradient Langevin Diffusions Kexin Jin Chenguang Liu J. Latz 13 0 0 23 May 2023
Kinetic Langevin MCMC Sampling Without Gradient Lipschitz Continuity -- the Strongly Convex Case Tim Johnston Iosif Lytras Sotirios Sabanis 13 8 0 19 Jan 2023
Global convergence of optimized adaptive importance samplers Ömer Deniz Akyildiz 9 7 0 02 Jan 2022
Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions Martin Hutzenthaler Arnulf Jentzen Katharina Pohl Adrian Riekert Luca Scarpa MLT 17 6 0 13 Dec 2021
Statistical Finite Elements via Langevin Dynamics Ömer Deniz Akyildiz Connor Duffin Sotirios Sabanis Mark Girolami 11 11 0 21 Oct 2021
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions Arnulf Jentzen Adrian Riekert 13 13 0 10 Aug 2021
Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo Vyacheslav Kungurtsev Adam D. Cobb T. Javidi Brian Jalaian 40 4 0 15 Jul 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 14 13 0 01 Apr 2021
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases Arnulf Jentzen T. Kröger ODL 12 7 0 23 Feb 2021
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions Patrick Cheridito Arnulf Jentzen Adrian Riekert Florian Rossmannek 11 24 0 19 Feb 2021
Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence Murat A. Erdogdu Rasa Hosseinzadeh Matthew Shunshi Zhang 73 41 0 22 Jul 2020
Multi-index Antithetic Stochastic Gradient Algorithm Mateusz B. Majka Marc Sabate Vidales Łukasz Szpruch 17 0 0 10 Jun 2020
Convergence rates for optimised adaptive importance samplers Ömer Deniz Akyildiz Joaquín Míguez 8 30 0 28 Mar 2019