Classification of pathological stage of prostate cancer patients using penalized splines. - Tathagata Banerjee; Tapabrata Maiti; Pushpal Mukhopadhyay (Comput. Stat. Data Anal., 2006)

An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). - Martin J. Aryee; José A. Gutiérrez-Pabello; Igor Kramnik; Tapabrata Maiti; John Quackenbush (BMC Bioinform., 2009)

Horvitz-Thompson Estimator. - Tapabrata Maiti (International Encyclopedia of Statistical Science, 2011)

A unified Bayesian approach for prediction and detection using mobile sensor networks. - Yunfei Xu; Jongeun Choi; Sarat C. Dass; Tapabrata Maiti (CDC, 2012)

Sequential Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks. - Yunfei Xu; Jongeun Choi; Sarat C. Dass; Tapabrata Maiti (IEEE Trans. Autom. Control., 2012)

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields. - Yunfei Xu; Jongeun Choi; Sarat C. Dass; Tapabrata Maiti (Autom., 2013)

Appearance-based localization using Group LASSO regression with an indoor experiment. - Huan N. Do; Jongeun Choi; Chae Young Lim; Tapabrata Maiti (AIM, 2015)

Spatial regression and estimation of disease risks: A clustering-based approach. - Wenning Feng; Chae Young Lim; Tapabrata Maiti; Zhen Zhang 052 (Stat. Anal. Data Min., 2016)

Bayesian model selection for generalized linear models using non-local priors. - Guiling Shi; Chae Young Lim; Tapabrata Maiti (Comput. Stat. Data Anal., 2019)

Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration. - Liangliang Zhang; Zhenxiang Jiang; Jongeun Choi; Chae Young Lim; Tapabrata Maiti; Seungik Baek (IEEE J. Biomed. Health Informatics, 2019)

Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges. - Shrijita Bhattacharya; Zihuan Liu; Tapabrata Maiti (CoRR, 2020)

A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease. - Zihuan Liu; Tapabrata Maiti; Andrew R. Bender (CoRR, 2020)

Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models. - Kaixu Yang; Tapabrata Maiti (Mach. Learn. Knowl. Extr., 2020)

TEC: Tensor Ensemble Classifier for Big Data. - Peide Li; Rejaul Karim; Tapabrata Maiti (CoRR, 2021)

Statistical foundation of Variational Bayes neural networks. - Shrijita Bhattacharya; Tapabrata Maiti (Neural Networks, 2021)

A fast and calibrated computer model emulator: an empirical Bayes approach. - Vojtech Kejzlar; Mookyong Son; Shrijita Bhattacharya; Tapabrata Maiti (Stat. Comput., 2021)

scSGL: kernelized signed graph learning for single-cell gene regulatory network inference. - Abdullah Karaaslanli; Satabdi Saha; Selin Aviyente; Tapabrata Maiti (Bioinform., 2022)

Sequential Bayesian Neural Subnetwork Ensembles. - Sanket R. Jantre; Sandeep Madireddy; Shrijita Bhattacharya; Tapabrata Maiti; Prasanna Balaprakash (CoRR, 2022)

Feature Selection integrated Deep Learning for Ultrahigh Dimensional and Highly Correlated Feature Space. - Arkaprabha Ganguli; Tapabrata Maiti (CoRR, 2022)

Coupled support tensor machine classification for multimodal neuroimaging data. - Peide Li; Seyyid Emre Sofuoglu; Selin Aviyente; Tapabrata Maiti (Stat. Anal. Data Min., 2022)

Multiple Signed Graph Learning for Gene Regulatory Network Inference. - Abdullah Karaaslanli; Satabdi Saha; Tapabrata Maiti; Selin Aviyente (ICASSP, 2023)

A comprehensive study of spike and slab shrinkage priors for structurally sparse Bayesian neural networks. - Sanket R. Jantre; Shrijita Bhattacharya; Tapabrata Maiti (CoRR, 2023)

Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration. - Vojtech Kejzlar; Tapabrata Maiti (Stat. Comput., 2023)

Layer adaptive node selection in Bayesian neural networks: Statistical guarantees and implementation details. - Sanket R. Jantre; Shrijita Bhattacharya; Tapabrata Maiti (Neural Networks, 2023)

Kernelized multiview signed graph learning for single-cell RNA sequencing data. - Abdullah Karaaslanli; Satabdi Saha; Tapabrata Maiti; Selin Aviyente (BMC Bioinform., 2023)

Statistically Valid Variational Bayes Algorithm for Ising Model Parameter Estimation. - Minwoo Kim; Shrijita Bhattacharya; Tapabrata Maiti (J. Comput. Graph. Stat., 2024)

A Tensor Based Varying-Coefficient Model for Multi-Modal Neuroimaging Data Analysis. - Pratim Guha Niyogi; Martin A. Lindquist; Tapabrata Maiti (IEEE Trans. Signal Process., 2024)

Variational Bayes Ensemble Learning Neural Networks With Compressed Feature Space. - Zihuan Liu; Shrijita Bhattacharya; Tapabrata Maiti (IEEE Trans. Neural Networks Learn. Syst., 2024)

Comprehensive study of variational Bayes classification for dense deep neural networks. - Shrijita Bhattacharya; Zihuan Liu; Tapabrata Maiti (Stat. Comput., 2024)

Error-controlled feature selection for ultrahigh-dimensional and highly correlated feature space using deep learning. - Arkaprabha Ganguli; Tapabrata Maiti; David Todem (Stat. Anal. Data Min., 2024)

Spike-and-Slab Shrinkage Priors for Structurally Sparse Bayesian Neural Networks. - Sanket R. Jantre; Shrijita Bhattacharya; Tapabrata Maiti (IEEE Trans. Neural Networks Learn. Syst., 2025)

Signed Graph Learning: Algorithms and Theory. - Abdullah Karaaslanli; Bisakh Banerjee; Tapabrata Maiti; Selin Aviyente (CoRR, 2025)