default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDThomas C. M. Lee
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 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) - [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
- [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) - [c13]Yue Kang, Cho-Jui Hsieh, Thomas Chun Man Lee:
Robust Lipschitz Bandits to Adversarial Corruptions. NeurIPS 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) - 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) - [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
- [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) - [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 - [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) - 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)
2010 – 2019
- 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) - [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) - 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) - 2011
- [r1]Jan Hannig, Hari Iyer, Thomas C. M. Lee:
Fiducial Inference. International Encyclopedia of Statistical Science 2011: 515-519 - 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) - [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
2000 – 2009
- 2009
- [c5]Thomas C. M. Lee, Zhengyuan Zhu:
Nonparametric spectral density estimation with missing observations. ICASSP 2009: 3041-3044 - 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) - [c4]Haonan Wang, Thomas C. M. Lee:
Curvilinear Feature Extraction for Noisy Point Pattern Images. ICME 2007: 1635-1638 - 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) - [c3]Thomas C. M. Lee, Xiao-Li Meng:
A Self-Consistent Wavelet Method for Denoising Images with Missing Pixels. ICASSP (2) 2005: 41-44 - 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)
1990 – 1999
- 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) - [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
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-23 20:34 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
