Xuan-Hong Dang

47 papers A* 8A 3B 4C 1Misc 1Journal 16Unranked 12

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	GneissWeb: Preparing High Quality Data for LLMs at Scale. CoRR Hajar Emami-Gohari, Swanand Ravindra Kadhe, Syed Yousaf Shah, Constantin Adam, Abdulhamid Adebayo, Praneet Adusumilli, Farhan Ahmed, Nathalie Baracaldo Angel, Santosh Borse, Yuan Chi Chang, Xuan-Hong Dang, Nirmit Desai, Revital Eres, Ran Iwamoto, Alexei Karve, Yan Koyfman, Wei-Han Lee, Changchang Liu, Boris Lublinsky, Takuya Ohko, Pablo Pesce, Maroun Touma, Shiqiang Wang, Shalisha Witherspoon, Herbert Woisetschlaeger, David Wood, Kun-Lung Wu, Issei Yoshida, Syed Zawad, Petros Zerfos, Yi Zhou, Bishwaranjan Bhattacharjee
2025	—	`conf`	Maximizing Information in Domain-Invariant Representation Improves Transfer Learning. CIC Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang, Ankush Singla, Yuhai Tu, Mark N. Wegman
2024	B	`conf`	Data-Prep-Kit: getting your data ready for LLM application development. IEEE Big Data David Wood, Boris Lublinsky, Alexy Roytman, Shivdeep Singh, Constantin Adam, Abdulhamid Adebayo, Sungeun An, Yuan Chi Chang, Xuan-Hong Dang, Nirmit Desai, Michele Dolfi, Hajar Emami-Gohari, Revital Eres, Takuya Goto, Dhiraj Joshi, Yan Koyfman, Mohammad Nassar, Hima Patel, Paramesvaran Selvam, Syed Yousaf Shah, Saptha Surendran, Daiki Tsuzuku, Petros Zerfos, Shahrokh Daijavad
2024	J	`jnl`	Data-Prep-Kit: getting your data ready for LLM application development. CoRR David Wood, Boris Lublinsky, Alexy Roytman, Shivdeep Singh, Abdulhamid Adebayo, Revital Eres, Mohammad Nassar, Hima Patel, Syed 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
2024	J	`jnl`	Granite Code Models: A Family of Open Foundation Models for Code Intelligence. CoRR 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, Syed 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
2024	—	`conf`	Modality-aware Transformer for Financial Time series Forecasting. ICAIF Hajar Emami-Gohari, Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2024	J	`jnl`	Scaling Granite Code Models to 128K Context. CoRR 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
2023	J	`jnl`	Maximal Domain Independent Representations Improve Transfer Learning. CoRR Adrian Shuai Li, Elisa Bertino, Xuan-Hong Dang, Ankush Singla, Yuhai Tu, Mark N. Wegman
2023	J	`jnl`	Modality-aware Transformer for Time series Forecasting. CoRR Hajar Emami, Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2021	—	`conf`	AutoAI-TS: AutoAI for Time Series Forecasting. SIGMOD Conference 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
2021	J	`jnl`	AutoAI-TS: AutoAI for Time Series Forecasting. CoRR 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
2020	A*	`conf`	"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering. IJCAI Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2019	J	`jnl`	"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering. CoRR Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2019	—	`conf`	seq2graph: Discovering Dynamic Non-linear Dependencies from Multivariate Time Series. IEEE BigData Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2018	—	`conf`	Learning Multiclassifiers with Predictive Features that Vary with Data Distribution. IEEE BigData Xuan-Hong Dang, Omid Askarisichani, Ambuj K. Singh
2018	—	`conf`	Root Cause Detection using Dynamic Dependency Graphs from Time Series Data. IEEE BigData Syed Yousaf Shah, Xuan-Hong Dang, Petros Zerfos
2018	—	`conf`	Unsupervised Threshold Autoencoder to Analyze and Understand Sentence Elements. IEEE BigData Xuan-Hong Dang, Raji Akella, Somaieh Bahrami, Vadim Sheinin, Petros Zerfos
2018	J	`jnl`	seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention. CoRR Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
2017	A*	`conf`	GPOP: Scalable Group-level Popularity Prediction for Online Content in Social Networks. WWW Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, Ambuj K. Singh
2017	A	`conf`	Subnetwork Mining with Spatial and Temporal Smoothness. SDM Xuan-Hong Dang, Hongyuan You, Ambuj K. Singh, Scott T. Grafton
2016	A*	`conf`	Graph Wavelets via Sparse Cuts. KDD Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K. Singh, Ananthram Swami
2016	J	`jnl`	Graph Wavelets via Sparse Cuts. CoRR Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K. Singh, Ananthram Swami
2016	A*	`conf`	Outlier Detection from Network Data with Subnetwork Interpretation. ICDM Xuan-Hong Dang, Arlei Silva, Ambuj K. Singh, Ananthram Swami, Prithwish Basu
2016	J	`jnl`	Outlier Detection from Network Data with Subnetwork Interpretation. CoRR Xuan-Hong Dang, Arlei Silva, Ambuj K. Singh, Ananthram Swami, Prithwish Basu
2015	J	`jnl`	A framework to uncover multiple alternative clusterings. Mach. Learn. Xuan-Hong Dang, James Bailey
2015	J	`jnl`	Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data. CoRR Xuan-Hong Dang, Ambuj K. Singh, Petko Bogdanov, Hongyuan You, Bayyuan Hsu
2015	A*	`conf`	Learning Predictive Substructures with Regularization for Network Data. ICDM Xuan-Hong Dang, Hongyuan You, Petko Bogdanov, Ambuj K. Singh
2014	—	`conf`	Discriminative Subnetworks with Regularized Spectral Learning for Global-State Network Data. ECML/PKDD (1) Xuan-Hong Dang, Ambuj K. Singh, Petko Bogdanov, Hongyuan You, Bayyuan Hsu
2014	A*	`conf`	Discriminative features for identifying and interpreting outliers. ICDE Xuan-Hong Dang, Ira Assent, Raymond T. Ng, Arthur Zimek, Erich Schubert
2014	J	`jnl`	Generating multiple alternative clusterings via globally optimal subspaces. Data Min. Knowl. Discov. Xuan-Hong Dang, James Bailey
2014	—	`conf`	On Critical Event Observability Using Social Networks: A Disaster Monitoring Perspective. MILCOM Dong-Anh Nguyen, Tarek F. Abdelzaher, Steven A. Borbash, Xuan-Hong Dang, Raghu K. Ganti, Ambuj K. Singh, Mudhakar Srivatsa
2013	A*	`conf`	Explaining Outliers by Subspace Separability. ICDM Barbora Micenková, Raymond T. Ng, Xuan-Hong Dang, Ira Assent
2013	—	`conf`	Local Outlier Detection with Interpretation. ECML/PKDD (3) Xuan-Hong Dang, Barbora Micenková, Ira Assent, Raymond T. Ng
2013	A	`conf`	Outlier Detection with Space Transformation and Spectral Analysis. SDM Ira Assent, Xuan-Hong Dang, Barbora Micenková, Raymond T. Ng
2012	—	`conf`	An Adaptive Algorithm for Finding Frequent Sets in Landmark Windows. SUM Xuan-Hong Dang, Kok-Leong Ong, Vincent C. S. Lee
2012	—	`conf`	Multiple Clustering Views via Constrained Projections. MultiClust@SDM Xuan-Hong Dang, Ira Assent, James Bailey
2010	A*	`conf`	A hierarchical information theoretic technique for the discovery of non linear alternative clusterings. KDD Xuan-Hong Dang, James Bailey
2010	A	`conf`	Generation of Alternative Clusterings Using the CAMI Approach. SDM Xuan-Hong Dang, James Bailey
2009	B	`conf`	An EM-Based Algorithm for Clustering Data Streams in Sliding Windows. DASFAA Xuan-Hong Dang, Vincent C. S. Lee, Wee Keong Ng, Arridhana Ciptadi, Kok-Leong Ong
2009	J	`jnl`	Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowl. Discov. Data Li Wan, Wee Keong Ng, Xuan-Hong Dang, Philip S. Yu, Kuan Zhang
2009	—	`ch.`	Frequent Sets Mining in Data Stream Environments. Encyclopedia of Data Warehousing and Mining Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vincent C. S. Lee
2009	C	`conf`	Incremental and Adaptive Clustering Stream Data over Sliding Window. DEXA Xuan-Hong Dang, Vincent C. S. Lee, Wee Keong Ng, Kok-Leong Ong
2008	—	`—`	Approximation algorithms for mining patterns from data streams Xuan-Hong Dang
2008	J	`jnl`	Online mining of frequent sets in data streams with error guarantee. Knowl. Inf. Syst. Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong
2007	Misc	`conf`	Discovering Frequent Sets from Data Streams with CPU Constraint. AusDM Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong, Vincent C. S. Lee
2006	B	`conf`	Adaptive Load Shedding for Mining Frequent Patterns from Data Streams. DaWaK Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong
2006	B	`conf`	EStream: Online Mining of Frequent Sets with Precise Error Guarantee. DaWaK Xuan-Hong Dang, Wee Keong Ng, Kok-Leong Ong