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A Complete Recipe for Stochastic Gradient MCMC

15 June 2015

Papers citing "A Complete Recipe for Stochastic Gradient MCMC"

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Title
Recent advances in deep learning theory Fengxiang He Dacheng Tao AI4CE 130 51 0 20 Dec 2020
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications Xuhui Meng H. Babaee George Karniadakis 67 132 0 19 Dec 2020
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent Kangqiao Liu Liu Ziyin Masakuni Ueda MLT 146 39 0 07 Dec 2020
State-Dependent Temperature Control for Langevin Diffusions Xuefeng Gao Z. Xu X. Zhou 94 28 0 15 Nov 2020
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng Guang Lin F. Liang BDL 78 28 0 19 Oct 2020
Bayesian Inference for Optimal Transport with Stochastic Cost Anton Mallasto Markus Heinonen Samuel Kaski OT 84 1 0 19 Oct 2020
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat Jennifer Gillenwater Eric Xing Afshin Rostamizadeh FedML 126 112 0 11 Oct 2020
MCMC-Interactive Variational Inference Quan Zhang Huangjie Zheng Mingyuan Zhou 49 1 0 02 Oct 2020
Action and Perception as Divergence Minimization Danijar Hafner Pedro A. Ortega Jimmy Ba Thomas Parr Karl J. Friston N. Heess 91 53 0 03 Sep 2020
Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC Denis Belomestny L. Iosipoi Eric Moulines A. Naumov S. Samsonov 72 6 0 16 Aug 2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC Wei Deng Qi Feng Liyao (Mars) Gao F. Liang Guang Lin BDL 77 47 0 12 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 84 112 0 10 Aug 2020
Tighter Generalization Bounds for Iterative Differentially Private Learning Algorithms Fengxiang He Bohan Wang Dacheng Tao FedML 55 18 0 18 Jul 2020
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference Hao Zhang Bo Chen Yulai Cong D. Guo Hongwei Liu Mingyuan Zhou BDL 64 28 0 15 Jun 2020
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty Miguel A. B. Monteiro Loic Le Folgoc Daniel Coelho De Castro Nick Pawlowski Bernardo Marques Konstantinos Kamnitsas Mark van der Wilk Ben Glocker UQCV BDL 47 115 0 10 Jun 2020
Bayesian Graph Neural Networks with Adaptive Connection Sampling Arman Hasanzadeh Ehsan Hajiramezanali Shahin Boluki Mingyuan Zhou N. Duffield Krishna R. Narayanan Xiaoning Qian BDL 89 118 0 07 Jun 2020
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics Zichao Wang Yi Gu Andrew Lan Richard Baraniuk 53 10 0 27 May 2020
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness Murat A. Erdogdu Rasa Hosseinzadeh 88 77 0 27 May 2020
Federated Stochastic Gradient Langevin Dynamics Khaoula El Mekkaoui Diego Mesquita P. Blomstedt Samuel Kaski FedML 77 24 0 23 Apr 2020
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent Dong Wang Xiaoqian Qin F. Song Li Cheng GAN 102 22 0 22 Apr 2020
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration Vojtech Kejzlar T. Maiti 56 6 0 28 Mar 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 247 796 0 13 Mar 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 37 0 0 02 Mar 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC Ruqi Zhang A. Feder Cooper Christopher De Sa 83 18 0 29 Feb 2020
On Thompson Sampling with Langevin Algorithms Eric Mazumdar Aldo Pacchiano Yi-An Ma Peter L. Bartlett Michael I. Jordan 67 11 0 23 Feb 2020
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients Ruilin Li Xin Wang H. Zha Molei Tao 34 4 0 20 Feb 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 95 61 0 19 Feb 2020
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization Ömer Deniz Akyildiz Sotirios Sabanis 97 17 0 13 Feb 2020
Learnable Bernoulli Dropout for Bayesian Deep Learning Shahin Boluki Randy Ardywibowo Siamak Zamani Dadaneh Mingyuan Zhou Xiaoning Qian BDL 63 34 0 12 Feb 2020
The Indian Chefs Process Patrick Dallaire L. Ambrogioni Ludovic Trottier Umut Güçlü Max Hinne Philippe Giguère B. Chaib-draa Marcel van Gerven François Laviolette 30 1 0 29 Jan 2020
The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson R. Salomone Fred Roosta 72 27 0 25 Jan 2020
Replica Exchange for Non-Convex Optimization Jing-rong Dong Xin T. Tong 108 21 0 23 Jan 2020
Recurrent Hierarchical Topic-Guided RNN for Language Generation D. Guo Bo Chen Ruiying Lu Mingyuan Zhou BDL LRM 80 8 0 21 Dec 2019
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling Ziming Liu Zheng Zhang 54 11 0 04 Dec 2019
Maximum entropy methods for texture synthesis: theory and practice Valentin De Bortoli A. Desolneux Alain Durmus B. Galerne Arthur Leclaire GAN 77 5 0 03 Dec 2019
Bayesian interpretation of SGD as Ito process Soma Yokoi Issei Sato 38 5 0 20 Nov 2019
Proximal Langevin Algorithm: Rapid Convergence Under Isoperimetry Andre Wibisono 140 49 0 04 Nov 2019
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo Bao Wang Difan Zou Quanquan Gu Stanley Osher BDL 54 9 0 02 Nov 2019
An Adaptive Empirical Bayesian Method for Sparse Deep Learning Wei Deng Xiao Zhang F. Liang Guang Lin BDL 126 44 0 23 Oct 2019
The Randomized Midpoint Method for Log-Concave Sampling Ruoqi Shen Y. Lee 125 118 0 12 Sep 2019
High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm Wenlong Mou Yian Ma Martin J. Wainwright Peter L. Bartlett Michael I. Jordan DiffM 81 85 0 28 Aug 2019
Bayesian Inference for Large Scale Image Classification Jonathan Heek Nal Kalchbrenner UQCV BDL 141 35 0 09 Aug 2019
Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement Ryutaro Tanno Daniel E. Worrall Enrico Kaden Aurobrata Ghosh Francesco Grussu A. Bizzi S. Sotiropoulos A. Criminisi Daniel C. Alexander MedIm DiffM 104 33 0 31 Jul 2019
Stochastic gradient Markov chain Monte Carlo Christopher Nemeth Paul Fearnhead BDL 81 139 0 16 Jul 2019
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout Zhilu Zhang Adrian Dalca M. Sabuncu UQCV BDL 59 48 0 23 Jun 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond Xuechen Li Denny Wu Lester W. Mackey Murat A. Erdogdu 93 71 0 19 Jun 2019
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision Fredrik K. Gustafsson Martin Danelljan Thomas B. Schon OOD UQCV BDL 82 301 0 04 Jun 2019
Accelerating Langevin Sampling with Birth-death Yulong Lu Jianfeng Lu J. Nolen 98 55 0 23 May 2019
Convolutional Poisson Gamma Belief Network Chaojie Wang Bo Chen Sucheng Xiao Mingyuan Zhou 87 15 0 14 May 2019
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics Xi Chen S. Du Xin T. Tong 77 33 0 30 Apr 2019