Wei Deng

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 28

Top Topics

Diffusion Models

Stochastic Gradient

Top Venues

J. Comput. Phys.

Publications

Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. AISTATS (2024)
Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo. CoRR (2024)
Haoyang Zheng, Hengrong Du, Qi Feng, Wei Deng, Guang Lin
Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics. CoRR (2024)
Wei Deng, Weijian Luo, Yixin Tan, Marin Bilos, Yu Chen, Yuriy Nevmyvaka, Ricky T. Q. Chen
Variational Schrödinger Diffusion Models. CoRR (2024)
Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin
Non-reversible Parallel Tempering for Deep Posterior Approximation. AAAI (2023)
Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
Interacting Contour Stochastic Gradient Langevin Dynamics. CoRR (2022)
Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
Interacting Contour Stochastic Gradient Langevin Dynamics. ICLR (2022)
Wei Deng, Guang Lin, Faming Liang
An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. Stat. Comput. 32 (4) (2022)
Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin
Non-reversible Parallel Tempering for Deep Posterior Approximation. CoRR (2022)
Wei Deng, Yi-An Ma, Zhao Song, Qian Zhang, Guang Lin
On Convergence of Federated Averaging Langevin Dynamics. CoRR (2021)
Yating Wang, Wei Deng, Guang Lin
Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications. J. Comput. Phys. 432 (2021)
Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. ICLR (2021)
Botao Hao, Tor Lattimore, Wei Deng
Information Directed Sampling for Sparse Linear Bandits. NeurIPS (2021)
Wei Deng, Junwei Pan, Tian Zhou, Deguang Kong, Aaron Flores, Guang Lin
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. WSDM (2021)
Botao Hao, Tor Lattimore, Wei Deng
Information Directed Sampling for Sparse Linear Bandits. CoRR (2021)
Yating Wang, Wei Deng, Guang Lin
An adaptive Hessian approximated stochastic gradient MCMC method. J. Comput. Phys. 432 (2021)
Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. ICML (2020)
Wei Deng, Guang Lin, Faming Liang
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. CoRR (2020)
Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. CoRR (2020)
Wei Deng, Guang Lin, Faming Liang
A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. NeurIPS (2020)
Yating Wang, Wei Deng, Guang Lin
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications. CoRR (2020)
Yating Wang, Wei Deng, Guang Lin
An adaptive Hessian approximated stochastic gradient MCMC method. CoRR (2020)
Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. CoRR (2020)
Wei Deng, Junwei Pan, Tian Zhou, Aaron Flores, Guang Lin
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. CoRR (2020)
Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. CoRR (2019)
Wei Deng, Xiao Zhang, Faming Liang, Guang Lin
An Adaptive Empirical Bayesian Method for Sparse Deep Learning. NeurIPS (2019)
Rongrong Zhang, Wei Deng, Yu Michael Zhu
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications. CoRR (2017)
Rongrong Zhang, Wei Deng, Yu Michael Zhu
Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications. ACML (2017)