Relaxed Simulated Tempering for VLSI Floorplan Designs. - Jason Cong; Tianming Kong; Dongmin Xu; Faming Liang; Jun S. Liu; Wing Hung Wong (ASP-DAC, 1999)

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 (ASP-DAC, 2000)

An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine. - Faming Liang (Neural Comput., 2003)

Efficient MCMC estimation of discrete distributions. - Faming Liang; Chuanhai Liu (Comput. Stat. Data Anal., 2005)

Evidence Evaluation for Bayesian Neural Networks Using Contour Monte Carlo. - Faming Liang (Neural Comput., 2005)

Bayesian neural networks for nonlinear time series forecasting. - Faming Liang (Stat. Comput., 2005)

Statistical and Computational Inverse Problems. - Faming Liang; Jianhua Huang (Technometrics, 2006)

Use of SVD-based probit transformation in clustering gene expression profiles. - Faming Liang (Comput. Stat. Data Anal., 2007)

Annealing stochastic approximation Monte Carlo algorithm for neural network training. - Faming Liang (Mach. Learn., 2007)

Dynamic agglomerative clustering of gene expression profiles. - Faming Liang; Naisyin Wang (Pattern Recognit. Lett., 2007)

Phylogenetic tree construction using sequential stochastic approximation Monte Carlo. - Sooyoung Cheon; Faming Liang (Biosyst., 2008)

Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems. - Yuan Ren 002; Yu Ding 002; Faming Liang (Stat. Comput., 2008)

Bayesian modeling of ChIP-chip data using latent variables. - Mingqi Wu; Faming Liang; Yanan Tian (BMC Bioinform., 2009)

Learning Bayesian networks for discrete data. - Faming Liang; Jian Zhang 008 (Comput. Stat. Data Anal., 2009)

Stochastic Approximation Monte Carlo for MLP Learning. - Faming Liang (Encyclopedia of Artificial Intelligence, 2009)

A hidden Ising model for ChIP-chip data analysis. - Qianxing Mo; Faming Liang (Bioinform., 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)

Folding small proteins via annealing stochastic approximation Monte Carlo. - Sooyoung Cheon; Faming Liang (Biosyst., 2011)

Annealing evolutionary stochastic approximation Monte Carlo for global optimization. - Faming Liang (Stat. Comput., 2011)

Statistical Properties of Horizontally Oriented Plates in Optically Thick Clouds From Satellite Observations. - Chen Zhou; Ping Yang 007; Andrew E. Dessler; Faming Liang (IEEE Geosci. Remote. Sens. Lett., 2013)

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants. - Faming Liang; Ick-Hoon Jin (Neural Comput., 2013)

Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants. - Ick-Hoon Jin; Faming Liang (Comput. Stat. Data Anal., 2014)

Bayesian Peak Picking for NMR Spectra. - Yichen Cheng; Xin Gao 001; Faming Liang (Genom. Proteom. Bioinform., 2014)

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities. - Sooyoung Cheon; Faming Liang; Yuguo Chen; Kai Yu (Stat. Comput., 2014)

Comment: "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization" by Gramacy et al. - Yichen Cheng; Faming Liang (Technometrics, 2016)

A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data. - Faming Liang; Jinsu Kim; Qifan Song (Technometrics, 2016)

Enhanced construction of gene regulatory networks using hub gene information. - Donghyeon Yu; Johan Lim; Xinlei Wang 001; Faming Liang; Guanghua Xiao (BMC Bioinform., 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)

Parallel and interacting stochastic approximation annealing algorithms for global optimisation. - Georgios Karagiannis; Bledar A. Konomi; Guang Lin 001; Faming Liang (Stat. Comput., 2017)

An Adaptive Empirical Bayesian Method for Sparse Deep Learning. - Wei Deng 002; Xiao Zhang; Faming Liang; Guang Lin 001 (NeurIPS, 2019)

Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data. - Suwa Xu; Bochao Jia; Faming Liang (Neural Comput., 2019)

Double-Parallel Monte Carlo for Bayesian analysis of big data. - Jingnan Xue; Faming Liang (Stat. Comput., 2019)

Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model. - Aiying Zhang; Jian Fang 001; Faming Liang; Vince D. Calhoun; Yu-Ping Wang 002 (IEEE J. Biomed. Health Informatics, 2019)

Non-convex Learning via Replica Exchange Stochastic Gradient MCMC. - Wei Deng 002; Qi Feng 005; Liyao Gao; Faming Liang; Guang Lin 001 (ICML, 2020)

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions. - Wei Deng 002; Guang Lin 001; Faming Liang (NeurIPS, 2020)

Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection. - Qifan Song; Yan Sun 011; Mao Ye; Faming Liang (CoRR, 2020)

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts. - Sehwan Kim; Qifan Song; Faming Liang (CoRR, 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 002 (IEEE Trans. Medical Imaging, 2020)

Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction. - Wei Deng 002; Qi Feng 005; Georgios Karagiannis; Guang Lin 001; Faming Liang (ICLR, 2021)

Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration. - Yan Sun 011; Wenjun Xiong; Faming Liang (NeurIPS, 2021)

Consistent Sparse Deep Learning: Theory and Computation. - Yan Sun 011; Qifan Song; Faming Liang (CoRR, 2021)

Nonlinear Variable Selection via Deep Neural Networks. - Yao Chen; Qingyi Gao; Faming Liang; Xiao Wang 045 (J. Comput. Graph. Stat., 2021)

Interacting Contour Stochastic Gradient Langevin Dynamics. - Wei Deng 002; Siqi Liang 005; Botao Hao; Guang Lin 001; Faming Liang (ICLR, 2022)

Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network. - Siqi Liang 005; Yan Sun 011; Faming Liang (NeurIPS, 2022)

A Kernel-Expanded Stochastic Neural Network. - Yan Sun 011; Faming Liang (CoRR, 2022)

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization. - Wei Deng 002; Guang Lin 001; Faming Liang (Stat. Comput., 2022)

Non-reversible Parallel Tempering for Deep Posterior Approximation. - Wei Deng 002; Qian Zhang 067; Qi Feng 005; Faming Liang; Guang Lin 001 (AAAI, 2023)

Sparse Deep Learning for Time Series Data: Theory and Applications. - Mingxuan Zhang; Yan Sun; Faming Liang (NeurIPS, 2023)

A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules. - Sehwan Kim; Qifan Song; Faming Liang (CoRR, 2023)

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols. - Fabrizio Cicala; Weicheng Wang; Tianhao Wang 001; Ninghui Li; Elisa Bertino; Faming Liang; Yang Yang (ACM Comput. Surv., 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., 2023)

Causal-StoNet: Causal Inference for High-Dimensional Complex Data. - Yaxin Fang; Faming Liang (ICLR, 2024)

Fast Value Tracking for Deep Reinforcement Learning. - Frank Shih; Faming Liang (ICLR, 2024)

Extended Fiducial Inference: Toward an Automated Process of Statistical Inference. - Faming Liang; Sehwan Kim; Yan Sun (CoRR, 2024)

Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior. - Mingxuan Zhang; Yan Sun; Faming Liang (CoRR, 2024)

Deep network embedding with dimension selection. - Tianning Dong; Yan Sun; Faming Liang (Neural Networks, 2024)

An extended Langevinized ensemble Kalman filter for non-Gaussian dynamic systems. - Peiyi Zhang; Tianning Dong; Faming Liang (Comput. Stat., 2024)

Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference. - Frank Shih; Zhenghao Jiang; Faming Liang (CoRR, 2025)

Extended fiducial inference for individual treatment effects via deep neural networks. - Sehwan Kim; Faming Liang (Stat. Comput., 2025)