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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
- 2024
[c27]Hajar Emami-Gohari, Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos:
Modality-aware Transformer for Financial Time series Forecasting. ICAIF 2024: 677-685- [i11]Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li, Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal Goyal, Hima Patel, S. Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar, Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen, Seetharami Seelam, Brian Belgodere, Carlos A. Fonseca, Amith Singhee, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda:
Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR abs/2405.04324 (2024) - [i10]Matt Stallone, Vaibhav Saxena, Leonid Karlinsky, Bridget McGinn, Tim Bula, Mayank Mishra, Adriana Meza Soria, Gaoyuan Zhang, Aditya Prasad, Yikang Shen, Saptha Surendran, Shanmukha C. Guttula, Hima Patel, Parameswaran Selvam, Xuan-Hong Dang, Yan Koyfman, Atin Sood, Rogério Feris, Nirmit Desai, David D. Cox, Ruchir Puri, Rameswar Panda:
Scaling Granite Code Models to 128K Context. CoRR abs/2407.13739 (2024) - [i9]David Wood, Boris Lublinsky, Alexy Roytman, Shivdeep Singh, Abdulhamid Adebayo, Revital Eres, Mohammad Nassar, Hima Patel, S. Yousaf Shah, Constantin Adam, Petros Zerfos, Nirmit Desai, Daiki Tsuzuku, Takuya Goto, Michele Dolfi, Saptha Surendran, Paramesvaran Selvam, Sungeun An, Yuan Chi Chang, Dhiraj Joshi, Hajar Emami-Gohari, Xuan-Hong Dang, Yan Koyfman, Shahrokh Daijavad:
Data-Prep-Kit: getting your data ready for LLM application development. CoRR abs/2409.18164 (2024) - 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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