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ORCIDXuan-Hong Dang
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- affiliation: University of California, Santa Barbara, Department of Computer Science, CA, USA
- affiliation: Aarhus University, Department of Computer Science, Denmark
- affiliation: University of Melbourne, Department of Computer Science and Software Engineerin, Australia
- affiliation: Institute of Infocomm Research, Singapore
- affiliation: Nanyang Technological University, School of Computer Engineering, Singapore
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2020 – today
- 2023
[i8]Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang, Ankush Singla, Yuhai Tu, Mark N. Wegman:
Maximal Domain Independent Representations Improve Transfer Learning. CoRR abs/2306.00262 (2023)- [i7]Hajar Emami, Xuan-Hong Dang, S. Yousaf Shah, Petros Zerfos:
Modality-aware Transformer for Time series Forecasting. CoRR abs/2310.01232 (2023) - 2021
- [c26]Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculín, Petros Zerfos:
AutoAI-TS: AutoAI for Time Series Forecasting. SIGMOD Conference 2021: 2584-2596 - [i6]Syed Yousaf Shah, Dhaval Patel, Long Vu, Xuan-Hong Dang, Bei Chen, Peter Kirchner, Horst Samulowitz, David Wood, Gregory Bramble, Wesley M. Gifford, Giridhar Ganapavarapu, Roman Vaculín, Petros Zerfos:
AutoAI-TS: AutoAI for Time Series Forecasting. CoRR abs/2102.12347 (2021) - 2020
- [c25]Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos:
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering. IJCAI 2020: 4597-4603
2010 – 2019
- 2019
- [c24]Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos:
seq2graph: Discovering Dynamic Non-linear Dependencies from Multivariate Time Series. IEEE BigData 2019: 1774-1783 - [i5]Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos:
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering. CoRR abs/1912.10858 (2019) - 2018
- [c23]Xuan-Hong Dang, Omid Askarisichani, Ambuj K. Singh:
Learning Multiclassifiers with Predictive Features that Vary with Data Distribution. IEEE BigData 2018: 673-682 - [c22]Syed Yousaf Shah, Xuan-Hong Dang, Petros Zerfos:
Root Cause Detection using Dynamic Dependency Graphs from Time Series Data. IEEE BigData 2018: 1998-2003 - [c21]Xuan-Hong Dang, Raji Akella, Somaieh Bahrami, Vadim Sheinin, Petros Zerfos:
Unsupervised Threshold Autoencoder to Analyze and Understand Sentence Elements. IEEE BigData 2018: 3267-3276 - [i4]Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos:
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention. CoRR abs/1812.04448 (2018) - 2017
- [c20]Xuan-Hong Dang, Hongyuan You, Ambuj K. Singh, Scott T. Grafton:
Subnetwork Mining with Spatial and Temporal Smoothness. SDM 2017: 354-362 - [c19]Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, Ambuj K. Singh:
GPOP: Scalable Group-level Popularity Prediction for Online Content in Social Networks. WWW 2017: 725-733 - 2016
- [c18]Xuan-Hong Dang, Arlei Silva, Ambuj K. Singh, Ananthram Swami, Prithwish Basu:
Outlier Detection from Network Data with Subnetwork Interpretation. ICDM 2016: 847-852 - [c17]Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K. Singh, Ananthram Swami:
Graph Wavelets via Sparse Cuts. KDD 2016: 1175-1184 - [i3]Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K. Singh, Ananthram Swami:
Graph Wavelets via Sparse Cuts. CoRR abs/1602.03320 (2016) - [i2]Xuan-Hong Dang, Arlei Silva, Ambuj K. Singh, Ananthram Swami, Prithwish Basu:
Outlier Detection from Network Data with Subnetwork Interpretation. CoRR abs/1610.00054 (2016) - 2015
- [j4]Xuan-Hong Dang, James Bailey:
A framework to uncover multiple alternative clusterings. Mach. Learn. 98(1-2): 7-30 (2015) - [c16]Xuan-Hong Dang, Hongyuan You, Petko Bogdanov, Ambuj K. Singh:
Learning Predictive Substructures with Regularization for Network Data. ICDM 2015: 81-90 - [i1]Xuan-Hong Dang, Ambuj K. Singh, Petko Bogdanov, Hongyuan You, Bayyuan Hsu:
Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data. CoRR abs/1512.06173 (2015) - 2014
- [j3]Xuan-Hong Dang, James Bailey:
Generating multiple alternative clusterings via globally optimal subspaces. Data Min. Knowl. Discov. 28(3): 569-592 (2014) - [c15]Xuan-Hong Dang, Ira Assent
, Raymond T. Ng, Arthur Zimek, Erich Schubert:
Discriminative features for identifying and interpreting outliers. ICDE 2014: 88-99 - [c14]Dong-Anh Nguyen, Tarek F. Abdelzaher, Steven A. Borbash, Xuan-Hong Dang, Raghu K. Ganti, Ambuj K. Singh, Mudhakar Srivatsa:
On Critical Event Observability Using Social Networks: A Disaster Monitoring Perspective. MILCOM 2014: 1633-1638 - [c13]Xuan-Hong Dang, Ambuj K. Singh, Petko Bogdanov, Hongyuan You, Bayyuan Hsu:
Discriminative Subnetworks with Regularized Spectral Learning for Global-State Network Data. ECML/PKDD (1) 2014: 290-306 - 2013
- [c12]Barbora Micenková, Raymond T. Ng, Xuan-Hong Dang, Ira Assent:
Explaining Outliers by Subspace Separability. ICDM 2013: 518-527 - [c11]Xuan-Hong Dang, Barbora Micenková, Ira Assent, Raymond T. Ng:
Local Outlier Detection with Interpretation. ECML/PKDD (3) 2013: 304-320 - [c10]Ira Assent, Xuan-Hong Dang, Barbora Micenková, Raymond T. Ng:
Outlier Detection with Space Transformation and Spectral Analysis. SDM 2013: 225-233 - 2012
- [c9]Xuan-Hong Dang, Ira Assent, James Bailey:
Multiple Clustering Views via Constrained Projections. MultiClust@SDM 2012: 23-30 - [c8]Xuan-Hong Dang, Kok-Leong Ong, Vincent C. S. Lee:
An Adaptive Algorithm for Finding Frequent Sets in Landmark Windows. SUM 2012: 590-597 - 2010
- [c7]Xuan-Hong Dang, James Bailey:
A hierarchical information theoretic technique for the discovery of non linear alternative clusterings. KDD 2010: 573-582 - [c6]Xuan-Hong Dang, James Bailey:
Generation of Alternative Clusterings Using the CAMI Approach. SDM 2010: 118-129
2000 – 2009
- 2009
- [j2]Li Wan, Wee Keong Ng, Xuan-Hong Dang, Philip S. Yu, Kuan Zhang:
Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowl. Discov. Data 3(3): 14:1-14:28 (2009) - [c5]Xuan-Hong Dang, Vincent C. S. Lee, Wee Keong Ng, Arridhana Ciptadi, Kok-Leong Ong:
An EM-Based Algorithm for Clustering Data Streams in Sliding Windows. DASFAA 2009: 230-235 - [c4]Xuan-Hong Dang, Vincent C. S. Lee, Wee Keong Ng, Kok-Leong Ong:
Incremental and Adaptive Clustering Stream Data over Sliding Window. DEXA 2009: 660-674 - [r1]Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vincent C. S. Lee:
Frequent Sets Mining in Data Stream Environments. Encyclopedia of Data Warehousing and Mining 2009: 901-906 - 2008
- [b1]Xuan-Hong Dang:
Approximation algorithms for mining patterns from data streams. Nanyang Technological University, Singapore, 2008 - [j1]Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong:
Online mining of frequent sets in data streams with error guarantee. Knowl. Inf. Syst. 16(2): 245-258 (2008) - 2007
- [c3]Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vincent C. S. Lee:
Discovering Frequent Sets from Data Streams with CPU Constraint. AusDM 2007: 121-128 - 2006
- [c2]Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong:
EStream: Online Mining of Frequent Sets with Precise Error Guarantee. DaWaK 2006: 312-321 - [c1]Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong:
Adaptive Load Shedding for Mining Frequent Patterns from Data Streams. DaWaK 2006: 342-351
Coauthor Index
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