A Stochastic Tessellation of Digital Space. - Thomas C. M. Lee; Richard Cowan (ISMM, 1994)

Nonparametric Estimation and Simulation of Two-Dimensional Gaussian Image Textures. - Thomas C. M. Lee; Mark Berman 002 (CVGIP Graph. Model. Image Process., 1997)

Segmenting Images Corrupted by Correlated Noise. - Thomas C. M. Lee (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Bandwidth selection for local linear regression: A simulation study. - Thomas C. M. Lee; Victor Solo (Comput. Stat., 1999)

Erratum to: "Bandwidth Selection for Local Linear Regression: A Simulation Study" 4/1999, pp 515-532. - Thomas C. M. Lee; Victor Solo (Comput. Stat., 2000)

A stabilized bandwidth selection method for kernel smoothing of the periodogram. - Thomas C. M. Lee (Signal Process., 2001)

Smoothing parameter selection for smoothing splines: a simulation study. - Thomas C. M. Lee (Comput. Stat. Data Anal., 2003)

Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms. - Thomas C. M. Lee; Tan F. Wong (Signal Process., 2003)

Automatic polynomial wavelet regression. - Thomas C. M. Lee; Hee-Seok Oh (Stat. Comput., 2004)

Kernel smoothing of periodograms under Kullback-Leibler discrepancy. - Jan Hannig; Thomas C. M. Lee (Signal Process., 2004)

A Self-Consistent Wavelet Method for Denoising Images with Missing Pixels. - Thomas C. M. Lee; Xiao-Li Meng (ICASSP (2), 2005)

Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment. - Hee-Seok Oh; Thomas C. M. Lee (Comput. Stat. Data Anal., 2005)

Model Selection for the Competing-Risks Model With and Without Masking. - Radu V. Craiu; Thomas C. M. Lee (Technometrics, 2005)

Automatic parameter selection for a k-segments algorithm for computing principal curves. - Haonan Wang; Thomas C. M. Lee (Pattern Recognit. Lett., 2006)

Data Adaptive Median Filters for Signal and Image Denoising Using a Generalized SURE Criterion. - Hsin-Cheng Huang; Thomas C. M. Lee (IEEE Signal Process. Lett., 2006)

Pattern Generation Using Likelihood Inference for Cellular Automata. - Radu V. Craiu; Thomas C. M. Lee (IEEE Trans. Image Process., 2006)

Curvilinear Feature Extraction for Noisy Point Pattern Images. - Haonan Wang; Thomas C. M. Lee (ICME, 2007)

Robust penalized regression spline fitting with application to additive mixed modeling. - Thomas C. M. Lee; Hee-Seok Oh (Comput. Stat., 2007)

Robust estimation of the self-similarity parameter in network traffic using wavelet transform. - Haipeng Shen; Zhengyuan Zhu; Thomas C. M. Lee (Signal Process., 2007)

Spectral Density Estimation Using Sharpened Periodograms. - Fang Yao; Thomas C. M. Lee (IEEE Trans. Signal Process., 2007)

Extraction of curvilinear features from noisy point patterns using principal curves. - Haonan Wang; Thomas C. M. Lee (Pattern Recognit. Lett., 2008)

Nonparametric spectral density estimation with missing observations. - Thomas C. M. Lee; Zhengyuan Zhu (ICASSP, 2009)

Statistically consistent image segmentation. - Alexander Aue; Thomas C. M. Lee (ICIP, 2010)

Stabilized thresholding with generalized sure for image denoising. - Hsin-Cheng Huang; Thomas C. M. Lee (ICIP, 2010)

On nonparametric local inference for density estimation. - Ngai Hang Chan; Thomas C. M. Lee; Liang Peng (Comput. Stat. Data Anal., 2010)

Structural break estimation of noisy sinusoidal signals. - Raymond K. W. Wong; Randy C. S. Lai; Thomas C. M. Lee (Signal Process., 2010)

Nonparametric cepstrum estimation via optimal risk smoothing. - Randy C. S. Lai; Thomas C. M. Lee; Raymond K. W. Wong; Fang Yao (IEEE Trans. Signal Process., 2010)

Fiducial Inference. - Jan Hannig; Hari Iyer; Thomas C. M. Lee (International Encyclopedia of Statistical Science, 2011)

Morphological feature extraction for statistical learning with applications to solar image data. - David C. Stenning; Thomas C. M. Lee; David A. Van Dyk; Vinay L. Kashyap; Julia Sandell; C. Alex Young (Stat. Anal. Data Min., 2013)

Computational issues of generalized fiducial inference. - Jan Hannig; Randy C. S. Lai; Thomas C. M. Lee (Comput. Stat. Data Anal., 2014)

Matrix Completion with Noisy Entries and Outliers. - Raymond K. W. Wong; Thomas C. M. Lee (J. Mach. Learn. Res., 2017)

High-dimensional variable selection in regression and classification with missing data. - Qi Gao; Thomas C. M. Lee (Signal Process., 2017)

Nonparametric modeling and break point detection for time series signal of counts. - Qi Gao; Thomas C. M. Lee; Chun Yip Yau (Signal Process., 2017)

Consistent Estimation for Partition-Wise Regression and Classification Models. - Rex C. Y. Cheung; Alexander Aue; Thomas C. M. Lee (IEEE Trans. Signal Process., 2017)

Block-wise Partitioning for Extreme Multi-label Classification. - Yuefeng Liang; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2018)

Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding. - Yao Li 015; Martin Renqiang Min; Wenchao Yu; Cho-Jui Hsieh; Thomas C. M. Lee; Erik Kruus (CoRR, 2018)

Segmenting Dynamic Network Data. - Rex C. Y. Cheung; Alexander Aue; Thomas C. M. Lee (CoRR, 2018)

Locally linear embedding with additive noise. - Justin Wang; Raymond K. W. Wong; Thomas C. M. Lee (Pattern Recognit. Lett., 2019)

Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting. - Miles E. Lopes; Suofei Wu; Thomas C. M. Lee (SIAM J. Math. Data Sci., 2020)

Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models. - Qi Gao; Randy C. S. Lai; Thomas C. M. Lee; Yao Li 015 (Technometrics, 2020)

Network Estimation via Graphon With Node Features. - Yi Su; Raymond K. W. Wong; Thomas C. M. Lee (IEEE Trans. Netw. Sci. Eng., 2020)

Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks. - Rex C. Y. Cheung; Alexander Aue; Seungyong Hwang; Thomas C. M. Lee (IEEE Trans. Signal Inf. Process. over Networks, 2020)

Towards Robustness of Deep Neural Networks via Regularization. - Yao Li 015; Martin Renqiang Min; Thomas C. M. Lee; Wenchao Yu; Erik Kruus; Wei Wang 010; Cho-Jui Hsieh (ICCV, 2021)

Detecting Adversarial Examples with Bayesian Neural Network. - Yao Li 015; Tongyi Tang; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2021)

A Review of Adversarial Attack and Defense for Classification Methods. - Yao Li 015; Minhao Cheng; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2021)

Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference. - Randy C. S. Lai; Jan Hannig; Thomas C. M. Lee (J. Comput. Graph. Stat., 2021)

Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling. - Zhenyu Wei; Raymond K. W. Wong; Thomas C. M. Lee (ICASSP, 2022)

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. - Qin Ding 002; Yue Kang 002; Yi-Wei Liu; Thomas Chun Man Lee; Cho-Jui Hsieh; James Sharpnack (NeurIPS, 2022)

Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. - Yue Kang 002; Cho-Jui Hsieh; Thomas Chun Man Lee (NeurIPS, 2022)

High-probability bounds for robust stochastic Frank-Wolfe algorithm. - Tongyi Tang; Krishna Balasubramanian; Thomas Chun Man Lee (UAI, 2022)

Uncertainty quantification for honest regression trees. - Suofei Wu; Jan Hannig; Thomas C. M. Lee (Comput. Stat. Data Anal., 2022)

Uncertainty Quantification in Graphon Estimation Using Generalized Fiducial Inference. - Yi Su; Jan Hannig; Thomas C. M. Lee (IEEE Trans. Signal Inf. Process. over Networks, 2022)

Statistical Consistency for Change Point Detection and Community Estimation in Time-Evolving Dynamic Networks. - Cong Xu; Thomas C. M. Lee (IEEE Trans. Signal Inf. Process. over Networks, 2022)

Uncertainty Quantification for Sparse Estimation of Spectral Lines. - Yi Han; Thomas C. M. Lee (IEEE Trans. Signal Process., 2022)

Change Point Detection and Node Clustering for Time Series of Graphs. - Cong Xu; Thomas C. M. Lee (IEEE Trans. Signal Process., 2022)

Robust Lipschitz Bandits to Adversarial Corruptions. - Yue Kang 002; Cho-Jui Hsieh; Thomas Chun Man Lee (NeurIPS, 2023)

Online Continuous Hyperparameter Optimization for Contextual Bandits. - Yue Kang 002; Cho-Jui Hsieh; Thomas C. M. Lee (CoRR, 2023)

High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference. - Zhenyu Wei; Thomas C. M. Lee (J. Comput. Graph. Stat., 2023)

Fast block-wise partitioning for extreme multi-label classification. - Yuefeng Liang; Cho-Jui Hsieh; Thomas C. M. Lee (Data Min. Knowl. Discov., 2023)

Low-rank Matrix Bandits with Heavy-tailed Rewards. - Yue Kang 002; Cho-Jui Hsieh; Thomas Chun Man Lee (UAI, 2024)

Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability. - Xiawei Wang; Yao Li 015; Cho-Jui Hsieh; Thomas C. M. Lee (IEEE Access, 2024)

Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging. - Xiawei Wang; James Sharpnack; Thomas C. M. Lee (CoRR, 2024)

Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits. - Yue Kang 002; Cho-Jui Hsieh; Thomas C. M. Lee (Trans. Mach. Learn. Res., 2024)

Structural Break Detection in Non-Stationary Network Vector Autoregression Models. - Yi Han; Thomas C. M. Lee (IEEE Trans. Netw. Sci. Eng., 2024)

Adversarial Examples Detection With Bayesian Neural Network. - Yao Li 015; Tongyi Tang; Cho-Jui Hsieh; Thomas C. M. Lee (IEEE Trans. Emerg. Top. Comput. Intell., 2024)