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2008.05367
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Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
International Conference on Machine Learning (ICML), 2020
12 August 2020
Wei Deng
Qi Feng
Liyao (Mars) Gao
F. Liang
Guang Lin
BDL
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Papers citing
"Non-convex Learning via Replica Exchange Stochastic Gradient MCMC"
35 / 35 papers shown
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Continuous Policy and Value Iteration for Stochastic Control Problems and Its Convergence
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JaxSGMC: Modular stochastic gradient MCMC in JAX
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16 May 2025
Accelerating Langevin Monte Carlo Sampling: A Large Deviations Analysis
Nian Yao
Pervez Ali
Xihua Tao
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24 Mar 2025
BLADE: Bayesian Langevin Active Discovery with Replica Exchange for Identification of Complex Systems
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Haoyang Zheng
Guang Lin
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04 Mar 2025
Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo
International Conference on Learning Representations (ICLR), 2025
Hyunsu Kim
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Chulhee Yun
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348
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02 Mar 2025
Enhancing Gradient-based Discrete Sampling via Parallel Tempering
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Yuhang Jia
Feng Zhou
521
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26 Feb 2025
Bayesian Computation in Deep Learning
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Bolian Li
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Yingzhen Li
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660
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25 Feb 2025
Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
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342
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01 Feb 2025
Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing
Giorgio Roffo
Carlo Biffi
Pietro Salvagnini
Andrea Cherubini
281
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05 Jul 2024
Feynman-Kac Operator Expectation Estimator
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Wei Liu
280
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02 Jul 2024
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics
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Haoyang Zheng
Hengrong Du
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Wei Deng
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276
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13 May 2024
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
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Riddhiman Bhattacharya
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201
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Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
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Amirhossein Mollaali
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23 Feb 2024
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
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Haoyang Zheng
Wei Deng
Christian Moya
Guang Lin
356
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0
22 Jan 2024
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions
Zhihao Kong
Amirhossein Mollaali
Christian Moya
Na Lu
Guang Lin
322
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0
28 Nov 2023
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
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Gregor Kasieczka
Maximilian F. Steffen
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369
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13 Oct 2023
Deep Operator Learning-based Surrogate Models with Uncertainty Quantification for Optimizing Internal Cooling Channel Rib Profiles
International Journal of Heat and Mass Transfer (IJHMT), 2023
Izzet Sahin
Christian Moya
Amirhossein Mollaali
Guang Lin
Guillermo Paniagua
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216
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01 Jun 2023
Subsampling Error in Stochastic Gradient Langevin Diffusions
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Kexin Jin
Chenguang Liu
J. Latz
335
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Fast Replica Exchange Stochastic Gradient Langevin Dynamics
Guanxun Li
Guang Lin
Zecheng Zhang
Quan Zhou
977
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05 Jan 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls
Journal of Chemical Theory and Computation (JCTC), 2022
Stephan Thaler
Gregor Doehner
Julija Zavadlav
390
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0
15 Dec 2022
Non-reversible Parallel Tempering for Deep Posterior Approximation
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Wei Deng
Qian Zhang
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300
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Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
Proceedings of the Royal Society A (Proc. R. Soc. A), 2022
Liyao (Mars) Gao
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217
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19 Nov 2022
Interacting Contour Stochastic Gradient Langevin Dynamics
International Conference on Learning Representations (ICLR), 2022
Wei Deng
Siqi Liang
Botao Hao
Guang Lin
F. Liang
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285
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20 Feb 2022
On Convergence of Federated Averaging Langevin Dynamics
Wei Deng
Qian Zhang
Yi-An Ma
Zhao Song
Guang Lin
FedML
483
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09 Dec 2021
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
Guang Lin
Christian Moya
Zecheng Zhang
276
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03 Nov 2021
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Journal of machine learning research (JMLR), 2021
Dong-Young Lim
Sotirios Sabanis
463
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0
28 May 2021
UPANets: Learning from the Universal Pixel Attention Networks
Entropy (Entropy), 2021
Ching-Hsun Tseng
Shin-Jye Lee
Jianxing Feng
Shengzhong Mao
Yuping Wu
Jia-Yu Shang
Mou-Chung Tseng
Xiao-Jun Zeng
202
17
0
15 Mar 2021
A New Framework for Variance-Reduced Hamiltonian Monte Carlo
Zhengmian Hu
Feihu Huang
Heng-Chiao Huang
143
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09 Feb 2021
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions
Neural Information Processing Systems (NeurIPS), 2020
Wei Deng
Guang Lin
F. Liang
BDL
476
35
0
19 Oct 2020
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Conference on Uncertainty in Artificial Intelligence (UAI), 2020
Difan Zou
Pan Xu
Quanquan Gu
423
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0
19 Oct 2020
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction
International Conference on Learning Representations (ICLR), 2020
Wei Deng
Qi Feng
G. Karagiannis
Guang Lin
F. Liang
354
9
0
02 Oct 2020
flexgrid2vec: Learning Efficient Visual Representations Vectors
Ali Hamdi
D. Kim
Flora D. Salim
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327
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Hessian-Free High-Resolution Nesterov Acceleration for Sampling
Ruilin Li
H. Zha
Molei Tao
474
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Stein Self-Repulsive Dynamics: Benefits From Past Samples
Neural Information Processing Systems (NeurIPS), 2020
Mao Ye
Zhaolin Ren
Qiang Liu
229
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21 Feb 2020
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