Faming Liang

According to our database, Faming Liang authored at least 53 papers between 1999 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Causal-StoNet: Causal Inference for High-Dimensional Complex Data.

Yaxin Fang, Faming Liang

CoRR, 2024

Fast Value Tracking for Deep Reinforcement Learning.

Frank Shih, Faming Liang

CoRR, 2024

2023

A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters.

Tianning Dong, Peiyi Zhang, Faming Liang

J. Comput. Graph. Stat., April, 2023

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols.

Fabrizio Cicala, Weicheng Wang, Tianhao Wang, Ninghui Li, Elisa Bertino, Faming Liang, Yang Yang

ACM Comput. Surv., 2023

A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.

Sehwan Kim, Qifan Song, Faming Liang

CoRR, 2023

Sparse Deep Learning for Time Series Data: Theory and Applications.

Mingxuan Zhang, Yan Sun, Faming Liang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Non-reversible Parallel Tempering for Deep Posterior Approximation.

Wei Deng, Qian Zhang, Qi Feng, Faming Liang, Guang Lin

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization.

Wei Deng, Guang Lin, Faming Liang

Stat. Comput., 2022

A Kernel-Expanded Stochastic Neural Network.

Yan Sun, Faming Liang

CoRR, 2022

Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network.

Siqi Liang, Yan Sun, Faming Liang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Interacting Contour Stochastic Gradient Langevin Dynamics.

Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Nonlinear Variable Selection via Deep Neural Networks.

Yao Chen, Qingyi Gao, Faming Liang, Xiao Wang

J. Comput. Graph. Stat., 2021

Consistent Sparse Deep Learning: Theory and Computation.

Yan Sun, Qifan Song, Faming Liang

CoRR, 2021

Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration.

Yan Sun, Wenjun Xiong, Faming Liang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction.

Wei Deng, Qi Feng, Georgios Karagiannis, Guang Lin, Faming Liang

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence.

Aiying Zhang, Biao Cai, Wenxing Hu, Bochao Jia, Faming Liang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang

IEEE Trans. Medical Imaging, 2020

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts.

Sehwan Kim, Qifan Song, Faming Liang

CoRR, 2020

Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection.

Qifan Song, Yan Sun, Mao Ye, Faming Liang

CoRR, 2020

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions.

Wei Deng, Guang Lin, Faming Liang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC.

Wei Deng, Qi Feng, Liyao Gao, Faming Liang, Guang Lin

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model.

Aiying Zhang, Jian Fang, Faming Liang, Vince D. Calhoun, Yu-Ping Wang

IEEE J. Biomed. Health Informatics, 2019

Double-Parallel Monte Carlo for Bayesian analysis of big data.

Jingnan Xue, Faming Liang

Stat. Comput., 2019

Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data.

Suwa Xu, Bochao Jia, Faming Liang

Neural Comput., 2019

An Adaptive Empirical Bayesian Method for Sparse Deep Learning.

Wei Deng, Xiao Zhang, Faming Liang, Guang Lin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2017

Parallel and interacting stochastic approximation annealing algorithms for global optimisation.

Georgios Karagiannis, Bledar A. Konomi, Guang Lin, Faming Liang

Stat. Comput., 2017

A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data.

Tianzhou Ma, Faming Liang, Steffi Oesterreich, George C. Tseng

J. Comput. Biol., 2017

Enhanced construction of gene regulatory networks using hub gene information.

Donghyeon Yu, Johan Lim, Xinlei Wang, Faming Liang, Guanghua Xiao

BMC Bioinform., 2017

2016

A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.

Faming Liang, Jinsu Kim, Qifan Song

Technometrics, 2016

Comment: "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization" by Gramacy et al.

Yichen Cheng, Faming Liang

Technometrics, 2016

2014

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities.

Sooyoung Cheon, Faming Liang, Yuguo Chen, Kai Yu

Stat. Comput., 2014

Bayesian Peak Picking for NMR Spectra.

Yichen Cheng, Xin Gao, Faming Liang

Genom. Proteom. Bioinform., 2014

Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants.

Ick-Hoon Jin, Faming Liang

Comput. Stat. Data Anal., 2014

2013

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants.

Faming Liang, Ick-Hoon Jin

Neural Comput., 2013

Statistical Properties of Horizontally Oriented Plates in Optically Thick Clouds From Satellite Observations.

Chen Zhou, Ping Yang, Andrew E. Dessler, Faming Liang

IEEE Geosci. Remote. Sens. Lett., 2013

2011

Annealing evolutionary stochastic approximation Monte Carlo for global optimization.

Faming Liang

Stat. Comput., 2011

Folding small proteins via annealing stochastic approximation Monte Carlo.

Sooyoung Cheon, Faming Liang

Biosyst., 2011

2010

Modeling the Relationship Between EDI Implementation and Firm Performance Improvement With Neural Networks.

G. Peter Zhang, Craig A. Hill, Yusen Xia, Faming Liang

IEEE Trans Autom. Sci. Eng., 2010

A hidden Ising model for ChIP-chip data analysis.

Qianxing Mo, Faming Liang

Bioinform., 2010

2009

Stochastic Approximation Monte Carlo for MLP Learning.

Faming Liang

Proceedings of the Encyclopedia of Artificial Intelligence (3 Volumes), 2009

Learning Bayesian networks for discrete data.

Faming Liang, Jian Zhang

Comput. Stat. Data Anal., 2009

Bayesian modeling of ChIP-chip data using latent variables.

Mingqi Wu, Faming Liang, Yanan Tian

BMC Bioinform., 2009

2008

Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems.

Yuan Ren, Yu Ding, Faming Liang

Stat. Comput., 2008

Phylogenetic tree construction using sequential stochastic approximation Monte Carlo.

Sooyoung Cheon, Faming Liang

Biosyst., 2008

2007

Dynamic agglomerative clustering of gene expression profiles.

Faming Liang, Naisyin Wang

Pattern Recognit. Lett., 2007

Annealing stochastic approximation Monte Carlo algorithm for neural network training.

Faming Liang

Mach. Learn., 2007

Use of SVD-based probit transformation in clustering gene expression profiles.

Faming Liang

Comput. Stat. Data Anal., 2007

2006

Statistical and Computational Inverse Problems.

Faming Liang, Jianhua Huang

Technometrics, 2006

2005

Bayesian neural networks for nonlinear time series forecasting.

Faming Liang

Stat. Comput., 2005

Evidence Evaluation for Bayesian Neural Networks Using Contour Monte Carlo.

Faming Liang

Neural Comput., 2005

Efficient MCMC estimation of discrete distributions.

Faming Liang, Chuanhai Liu

Comput. Stat. Data Anal., 2005

2003

An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine.

Faming Liang

Neural Comput., 2003

2000

Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application.

Jason Cong, Tianming Kong, Faming Liang, Jun S. Liu, Wing Hung Wong, Dongmin Xu

Proceedings of ASP-DAC 2000, 2000

1999

Relaxed Simulated Tempering for VLSI Floorplan Designs.

Jason Cong, Tianming Kong, Dongmin Xu, Faming Liang, Jun S. Liu, Wing Hung Wong

Proceedings of the 1999 Conference on Asia South Pacific Design Automation, 1999