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Journal Articles
- 2024
[j44]Xiawei Wang
, Yao Li, Cho-Jui Hsieh, Thomas C. M. Lee:
Uncovering Distortion Differences: A Study of Adversarial Attacks and Machine Discriminability. IEEE Access 12: 117872-117883 (2024)- [j43]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Adversarial Examples Detection With Bayesian Neural Network. IEEE Trans. Emerg. Top. Comput. Intell. 8(5): 3654-3664 (2024) - [j42]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits. Trans. Mach. Learn. Res. 2024 (2024) - [j41]Yi Han, Thomas C. M. Lee:
Structural Break Detection in Non-Stationary Network Vector Autoregression Models. IEEE Trans. Netw. Sci. Eng. 11(5): 4134-4145 (2024) - 2023
- [j40]Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee:
Fast block-wise partitioning for extreme multi-label classification. Data Min. Knowl. Discov. 37(6): 2192-2215 (2023) - [j39]Zhenyu Wei, Thomas C. M. Lee:
High-Dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference. J. Comput. Graph. Stat. 32(1): 226-240 (2023) - 2022
- [j38]Suofei Wu, Jan Hannig, Thomas C. M. Lee:
Uncertainty quantification for honest regression trees. Comput. Stat. Data Anal. 167: 107377 (2022) - [j37]Cong Xu, Thomas C. M. Lee:
Statistical Consistency for Change Point Detection and Community Estimation in Time-Evolving Dynamic Networks. IEEE Trans. Signal Inf. Process. over Networks 8: 215-227 (2022) - [j36]Yi Su, Jan Hannig, Thomas C. M. Lee:
Uncertainty Quantification in Graphon Estimation Using Generalized Fiducial Inference. IEEE Trans. Signal Inf. Process. over Networks 8: 597-609 (2022) - [j35]Cong Xu, Thomas C. M. Lee:
Change Point Detection and Node Clustering for Time Series of Graphs. IEEE Trans. Signal Process. 70: 3165-3180 (2022) - [j34]Yi Han, Thomas C. M. Lee:
Uncertainty Quantification for Sparse Estimation of Spectral Lines. IEEE Trans. Signal Process. 70: 6243-6256 (2022) - 2021
- [j33]Randy C. S. Lai, Jan Hannig, Thomas C. M. Lee:
Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference. J. Comput. Graph. Stat. 30(4): 934-945 (2021) - 2020
- [j32]Miles E. Lopes, Suofei Wu, Thomas C. M. Lee:
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting. SIAM J. Math. Data Sci. 2(4): 921-943 (2020) - [j31]Qi Gao, Randy C. S. Lai, Thomas C. M. Lee, Yao Li:
Uncertainty Quantification for High-Dimensional Sparse Nonparametric Additive Models. Technometrics 62(4): 513-524 (2020) - [j30]Yi Su, Raymond K. W. Wong, Thomas C. M. Lee:
Network Estimation via Graphon With Node Features. IEEE Trans. Netw. Sci. Eng. 7(3): 2078-2089 (2020) - [j29]Rex C. Y. Cheung, Alexander Aue, Seungyong Hwang, Thomas C. M. Lee:
Simultaneous Detection of Multiple Change Points and Community Structures in Time Series of Networks. IEEE Trans. Signal Inf. Process. over Networks 6: 580-591 (2020) - 2019
- [j28]Justin Wang, Raymond K. W. Wong, Thomas C. M. Lee:
Locally linear embedding with additive noise. Pattern Recognit. Lett. 123: 47-52 (2019) - 2017
- [j27]Raymond K. W. Wong, Thomas C. M. Lee:
Matrix Completion with Noisy Entries and Outliers. J. Mach. Learn. Res. 18: 147:1-147:25 (2017) - [j26]Qi Gao, Thomas C. M. Lee:
High-dimensional variable selection in regression and classification with missing data. Signal Process. 131: 1-7 (2017) - [j25]Qi Gao, Thomas C. M. Lee, Chun Yip Yau:
Nonparametric modeling and break point detection for time series signal of counts. Signal Process. 138: 307-312 (2017) - [j24]Rex C. Y. Cheung, Alexander Aue, Thomas C. M. Lee:
Consistent Estimation for Partition-Wise Regression and Classification Models. IEEE Trans. Signal Process. 65(14): 3662-3674 (2017) - 2014
- [j23]Jan Hannig, Randy C. S. Lai, Thomas C. M. Lee:
Computational issues of generalized fiducial inference. Comput. Stat. Data Anal. 71: 849-858 (2014) - 2013
- [j22]David C. Stenning, Thomas C. M. Lee, David A. Van Dyk, Vinay Kashyap, Julia Sandell, C. Alex Young:
Morphological feature extraction for statistical learning with applications to solar image data. Stat. Anal. Data Min. 6(4): 329-345 (2013) - 2010
- [j21]Ngai Hang Chan, Thomas C. M. Lee, Liang Peng:
On nonparametric local inference for density estimation. Comput. Stat. Data Anal. 54(2): 509-515 (2010) - [j20]Raymond K. W. Wong, Randy C. S. Lai, Thomas C. M. Lee:
Structural break estimation of noisy sinusoidal signals. Signal Process. 90(1): 303-312 (2010) - [j19]Randy C. S. Lai, Thomas C. M. Lee, Raymond K. W. Wong, Fang Yao:
Nonparametric cepstrum estimation via optimal risk smoothing. IEEE Trans. Signal Process. 58(3): 1507-1514 (2010) - 2008
- [j18]Haonan Wang, Thomas C. M. Lee:
Extraction of curvilinear features from noisy point patterns using principal curves. Pattern Recognit. Lett. 29(16): 2078-2084 (2008) - 2007
- [j17]Thomas C. M. Lee, Hee-Seok Oh:
Robust penalized regression spline fitting with application to additive mixed modeling. Comput. Stat. 22(1): 159-171 (2007) - [j16]Haipeng Shen, Zhengyuan Zhu, Thomas C. M. Lee:
Robust estimation of the self-similarity parameter in network traffic using wavelet transform. Signal Process. 87(9): 2111-2124 (2007) - [j15]Fang Yao, Thomas C. M. Lee:
Spectral Density Estimation Using Sharpened Periodograms. IEEE Trans. Signal Process. 55(9): 4711-4716 (2007) - 2006
- [j14]Haonan Wang, Thomas C. M. Lee:
Automatic parameter selection for a k-segments algorithm for computing principal curves. Pattern Recognit. Lett. 27(10): 1142-1150 (2006) - [j13]Hsin-Cheng Huang, Thomas C. M. Lee:
Data Adaptive Median Filters for Signal and Image Denoising Using a Generalized SURE Criterion. IEEE Signal Process. Lett. 13(9): 561-564 (2006) - [j12]Radu V. Craiu, Thomas C. M. Lee:
Pattern Generation Using Likelihood Inference for Cellular Automata. IEEE Trans. Image Process. 15(7): 1718-1727 (2006) - 2005
- [j11]Hee-Seok Oh, Thomas C. M. Lee:
Hybrid local polynomial wavelet shrinkage: wavelet regression with automatic boundary adjustment. Comput. Stat. Data Anal. 48(4): 809-819 (2005) - [j10]Radu V. Craiu, Thomas C. M. Lee:
Model Selection for the Competing-Risks Model With and Without Masking. Technometrics 47(4): 457-467 (2005) - 2004
- [j9]Thomas C. M. Lee, Hee-Seok Oh:
Automatic polynomial wavelet regression. Stat. Comput. 14(4): 337-341 (2004) - [j8]Jan Hannig, Thomas C. M. Lee:
Kernel smoothing of periodograms under Kullback-Leibler discrepancy. Signal Process. 84(7): 1255-1266 (2004) - 2003
- [j7]Thomas C. M. Lee:
Smoothing parameter selection for smoothing splines: a simulation study. Comput. Stat. Data Anal. 42(1-2): 139-148 (2003) - [j6]Thomas C. M. Lee, Tan F. Wong:
Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms. Signal Process. 83(1): 79-90 (2003) - 2001
- [j5]Thomas C. M. Lee:
A stabilized bandwidth selection method for kernel smoothing of the periodogram. Signal Process. 81(2): 419-430 (2001) - 2000
- [j4]Thomas C. M. Lee, Victor Solo:
Erratum to: "Bandwidth Selection for Local Linear Regression: A Simulation Study" 4/1999, pp 515-532. Comput. Stat. 15(2) (2000) - 1999
- [j3]Thomas C. M. Lee, Victor Solo:
Bandwidth selection for local linear regression: A simulation study. Comput. Stat. 14(4): 515-532 (1999) - 1998
- [j2]Thomas C. M. Lee:
Segmenting Images Corrupted by Correlated Noise. IEEE Trans. Pattern Anal. Mach. Intell. 20(5): 481-492 (1998) - 1997
- [j1]Thomas C. M. Lee, Mark Berman:
Nonparametric Estimation and Simulation of Two-Dimensional Gaussian Image Textures. CVGIP Graph. Model. Image Process. 59(6): 434-445 (1997)
Conference and Workshop Papers
- 2023
- [c13]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. NeurIPS 2023 - 2022
- [c12]Zhenyu Wei, Raymond K. W. Wong, Thomas C. M. Lee:
Extending the Use of MDL for High-Dimensional Problems: Variable Selection, Robust Fitting, and Additive Modeling. ICASSP 2022: 5707-5711 - [c11]Qin Ding, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack:
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms. NeurIPS 2022 - [c10]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. NeurIPS 2022 - [c9]Tongyi Tang, Krishna Balasubramanian, Thomas Chun Man Lee:
High-probability bounds for robust stochastic Frank-Wolfe algorithm. UAI 2022: 1917-1927 - 2021
- [c8]Yao Li, Martin Renqiang Min, Thomas C. M. Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh:
Towards Robustness of Deep Neural Networks via Regularization. ICCV 2021: 7476-7485 - 2010
- [c7]Hsin-Cheng Huang, Thomas C. M. Lee:
Stabilized thresholding with generalized sure for image denoising. ICIP 2010: 1881-1884 - [c6]Alexander Aue, Thomas C. M. Lee:
Statistically consistent image segmentation. ICIP 2010: 2229-2232 - 2009
- [c5]Thomas C. M. Lee, Zhengyuan Zhu:
Nonparametric spectral density estimation with missing observations. ICASSP 2009: 3041-3044 - 2007
- [c4]Haonan Wang, Thomas C. M. Lee:
Curvilinear Feature Extraction for Noisy Point Pattern Images. ICME 2007: 1635-1638 - 2005
- [c3]Thomas C. M. Lee, Xiao-Li Meng:
A Self-Consistent Wavelet Method for Denoising Images with Missing Pixels. ICASSP (2) 2005: 41-44 - 1997
- [c2]Thomas C. M. Lee:
Segmenting Images Corrupted by Correlated Noise. ICIP (1) 1997: 247-250 - 1994
- [c1]Thomas C. M. Lee, Richard Cowan:
A Stochastic Tessellation of Digital Space. ISMM 1994: 217-224
Reference Works
- 2011
- [r1]Jan Hannig, Hari Iyer, Thomas C. M. Lee:
Fiducial Inference. International Encyclopedia of Statistical Science 2011: 515-519
Informal and Other Publications
- 2024
- [i11]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems. CoRR abs/2401.07298 (2024) - [i10]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Low-rank Matrix Bandits with Heavy-tailed Rewards. CoRR abs/2404.17709 (2024) - [i9]Xiawei Wang, James Sharpnack, Thomas C. M. Lee:
Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging. CoRR abs/2408.09367 (2024) - 2023
- [i8]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Contextual Bandits. CoRR abs/2302.09440 (2023) - [i7]Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. CoRR abs/2305.18543 (2023) - 2021
- [i6]Yao Li, Tongyi Tang, Cho-Jui Hsieh, Thomas C. M. Lee:
Detecting Adversarial Examples with Bayesian Neural Network. CoRR abs/2105.08620 (2021) - [i5]Yao Li, Minhao Cheng, Cho-Jui Hsieh, Thomas C. M. Lee:
A Review of Adversarial Attack and Defense for Classification Methods. CoRR abs/2111.09961 (2021) - 2019
- [i4]Miles E. Lopes, Suofei Wu, Thomas C. M. Lee:
Measuring the Algorithmic Convergence of Randomized Ensembles: The Regression Setting. CoRR abs/1908.01251 (2019) - 2018
- [i3]Yuefeng Liang, Cho-Jui Hsieh, Thomas C. M. Lee:
Block-wise Partitioning for Extreme Multi-label Classification. CoRR abs/1811.01305 (2018) - [i2]Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas C. M. Lee, Erik Kruus:
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding. CoRR abs/1811.07950 (2018) - [i1]Rex C. Y. Cheung, Alexander Aue, Thomas C. M. Lee:
Segmenting Dynamic Network Data. CoRR abs/1812.00789 (2018)
Coauthor Index
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