Wiki Contents


Conference Papers (Refereed)

Topics

All Topics
Natural Language Processing
Computer Vision
Reinforcement Learning
Deep Learning
Data Mining
Probabilistic Reasoning
Genetic and Evolutionary Computation
High Performance Computing
Network Science
Machine Learning
Health Medical
Speech Recognition
After Before
  1. Computer Vision
    Deep Learning

    Luo, L. & Hsu, W. (2022). AMMUNIT: An Attention-based Multimodal Multi-domain UNsupervised Image-to-image Translation Framework. In Proceedings of the 31st International Conference on Artificial Neural Networks (ICANN 2022), Bristol, UK, September 6-9, 2022.

  2. Computer Vision
    Deep Learning

    Okerinde, A., Hoggatt, S., Lakkireddy, D., Brubaker, N., Hsu, W., Spiesman, B. & Shamir, L. (2022). Self-Supervised Approach to Addressing Zero-Shot Learning Problem. arXiv preprint arXiv:2201.01391. In Proceedings of 4th International Conference on Computing and Data Science (CONF-CDS 2022), July 16, 2022 (Virtual conference).

  3. Computer Vision
    Deep Learning

    Nafi, N.M., Dietrich, S., & Hsu, W. (2022). Risky Tackle Detection from American Football Practice Videos using 3D Convolutional Networks. In Proceedings of 18th International Conference on Machine Learning and Data Mining (MLDM 2022), New Jersey, USA, July 16-21, 2022.

  4. Data Mining
    High Performance Computing
    Machine Learning

    Andresen, D., Tanash, M., Bohn, C., & Hsu, W. (2022). Cost-Effective Resource Provisioning of Cloud Computing via Supervised Machine Learning. In Proceedings of the 2022 World Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE 2022), Las Vegas, NV, USA, July 25-28, 2022.

  5. Natural Language Processing
    Deep Learning
    Machine Learning

    Yang, H., Aguirre, C. A., Hsu, W. (2022). PIEKM: ML-based Procedural Information Extraction and Knowledge Management System for Materials Science Literature. In Proceedings of the 2st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations (AACL-IJCNLP 2022), November 20-23, 2022, virtual conference.

  6. Natural Language Processing
    Deep Learning

    Martin, C., Yang, H., & Hsu, W. (2022). KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), Seattle, USA, July 14-15, 2022.

  7. Computer Vision
    Deep Learning

    Alsadhan, M. & Hsu, W. H. (2022). Few-Shot Learning in Object Classification using Meta-Learning with Between-Class Attribute Transfer. In Proceedings of the 14th ACM International Conference on Machine Learning and Computing (ICMLC 2022), p. 560–565, Guangzhou, China, February 18-21, 2022.

  8. Natural Language Processing
    Deep Learning

    Yang, H. & Hsu, W. (2022). Transformer-based Approach for Document Layout Understanding. In Proceedings of the 29th IEEE International Conference on Image (ICIP 2022), Bordeaux, France, October 16-19, 2022, to appear.

  9. Reinforcement Learning
    Deep Learning

    Nafi, N. M., Glasscock, C., & Hsu, W. H. (2022). Attention-based Partial Decoupling of Policy and Value for Generalization in Reinforcement Learning. In Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA 2022), Nassau, The Bahamas, December 12-15, 2022, to appear.

  10. Data Mining
    High Performance Computing
    Machine Learning

    Hutchison, S., Andresen, D., Neilsen, M., Hsu, W., & Parsons, B. (2022). High Performance Computing Queue Time Prediction using Clustering and Regression. In Proceedings of the 1st Workshop on Applications of Machine Learning and Artificial Intelligence in High-Performance Computing (WAML-HPC 2022), Gdansk, Poland, September 11-14, 2022.

  11. Computer Vision
    Deep Learning

    Luo, L., Hsu, W. H, & Wang, S-X. (2021). UNMMIT: A Unified Framework on Unsupervised Multimodal Multi-Domain Image-to-Image Translation. In Proceedings of the 10th International Conference on Computing and Pattern Recognition (ICCPR 2021), Shanghai, China, Virtual conference, October 15-17, 2021.

  12. Computer Vision
    Deep Learning

    Okerinde, A., Theis, T., Nafi N., Hsu, W., & Shamir, L. (2021). eGAN: Unsupervised approach to class imbalance using transfer learning. arXiv preprint arXiv:2104.04162. In Proceedings of 19th International Conference on Computer Analysis of Images and Patterns (CAIP 2021), September 27 - October 1, 2021, Virtual conference.

  13. Deep Learning
    High Performance Computing

    Bose, A., Yang, H., Hsu, W. H., & Andresen, D. (2021). HPCGCN: A Predictive Framework on High Performance Computing Cluster Log Data Using Graph Convolutional Networks. In Proceedings of the 8th International Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraph 2021), held in conjunction with the IEEE International Conference on Big Data 2021 (IEEE BigData 2021), virtual conference, December 15-18, 2021.

  14. Computer Vision
    Deep Learning

    Luo, L., Hsu, W. H, & Wang, S-X. (2021). Towards Fine-Grained Control over Latent Space for Unpaired Image-to-Image Translation. In 30th International Conference on Artificial Neural Networks (ICANN 2021), Virtual conference, 14 – 17 September 2021.

  15. Natural Language Processing
    Deep Learning

    Yang, H. & Hsu, W. H. (2021). Named Entity Recognition from Procedural Text on Materials Synthesis using an Attention-Based Approach. Proceedings of the 2021 Workshop on Scientific Document Understanding at the 35th International Conference of the Association for the Advancement of Artificial Intelligence (SDU@AAAI-21), virtual conference.

  16. Computer Vision
    Deep Learning

    Okerinde, A., Hsu, W., Shamir, L., & Theis, T. (2021). AdeNet: Deep learning architecture that identifies damaged electrical insulators in power lines. arXiv preprint arXiv:2103.01426. In Proceedings of the 17th International Conference on Machine Learning and Data Mining (MLDM 2021), Newark, NJ, USA, July 18-22, 2021.

  17. Natural Language Processing
    Computer Vision
    Deep Learning
    Data Mining

    Yang, H. & Hsu, W. H. (2021). Automatic Metadata Information Extraction from Scientific Literature using Deep Neural Networks. In Proceedings of the 14th International Conference on Machine Vision (ICMV 2021), Rome, Italy (virtual conference), November 8-12, 2021.

  18. Data Mining
    High Performance Computing

    Tanash, M., Yang, H., Andresen, D., & Hsu, W. (2021). Ensemble Prediction of Job Resources to Improve System Performance for Slurm-Based HPC Systems (Andrews Award, Best Full Paper Award) Proceedings of Practice and Experience in Advanced Research Computing (PEARC 2021), July 19 - 22, 2021. Virtual conference.

  19. Data Mining
    High Performance Computing

    Tanash, M., Andresen, D., & Hsu, W. (2021). AMPRO-HPCC: A Machine-Learning Tool for Predicting Resources on Slurm HPC Clusters. In Proceeedings of the 15th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2021), Barcelona, Spain, October 3 - 7, 2021.

  20. Natural Language Processing
    Deep Learning

    Lamba, D. & Hsu, W. H. (2021). Answer-Agnostic Question Generation in Privacy Policy Domain using Sequence-to-Sequence and Transformer Models. In 4th International Conference on Machine Learning and Natural Language Processing (MLNLP 2021), Sanya, China (hybrid conference), December 27-29, 2021.

  21. Natural Language Processing
    Network Science

    Bose, A., Gopal Sundari, S., Behzadan, V., & Hsu, W. (2021). Tracing Relevant Twitter Accounts Active in Cyber Threat Intelligence Domain by Exploiting Content and Structure of Twitter Network. In Proceedings of the 19th IEEE International Conference on Intelligence and Security Informatics (ISI 2021), San Antonio, TX, USA, November 2-3, 2021.

  22. Natural Language Processing
    Deep Learning

    Lamba, D. & Hsu, W. H. (2021). Constraint-Based Neural Question Generation using Sequence-to-Sequence and Transformer Models for Privacy Policy Documents. In Proceedings of the 10th International Conference on Knowledge Discovery (ICKD 2021), Macau, China (hybrid conference), November 27 - 29, 2021.

  23. Computer Vision
    Deep Learning

    Nafi, N.M. & Hsu, W. (2020). Addressing Class Imbalance in Image-Based Plant Disease Detection: Deep Generative vs. Sampling-Based Approaches. (Best Paper Award) Proceedings of the 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020), Niterói, Brazil, July 1-3, 2020.

  24. Natural Language Processing
    Reinforcement Learning

    Nafi, N. M., Bose, A., Khanal, S., Caragea, D., & Hsu, W. (2020). Abstractive Text Summarization of Disaster-Related Documents - Work in Progress (WiP) paper. In Proceedings of the 17th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2020), Blacksburg, Virginia, USA, May 24 - 27, 2020.

  25. Computer Vision
    Deep Learning

    Luo, L., Hsu, W. & Wang, S. (2020). Shape-aware Generative Adversarial Networks for Attribute Transfer, The 13th International Conference on Machine Vision (ICMV 2020), Rome, Italy, November 2-6, 2020.

  26. Data Mining
    High Performance Computing

    Okanlawon, A., Yang, H., Bose, A., Hsu, W., Andresen, D., & Tanash, M. (2020). Feature Selection for Learning to Predict Outcomes of Compute Cluster Jobs with Application to Decision Support. Proceedings of the International Conference on Computational Science and Computational Intelligence Symposium on Parallel & Distributed Computing (CSCI-ISPD 2020), virtual conference.

  27. Computer Vision
    Deep Learning

    Luo, L., Hsu, W., & Wang, S. (2020). Data Augmentation Using Generative Adversarial Networks for Electrical Insulator Anomaly Detection. Proceedings of the International Conference on Industrial Engineering and Artificial Intelligence (IEAI 2020), April 7 - 9, 2020.

  28. Natural Language Processing
    Computer Vision
    Deep Learning

    Yang, H. & Hsu, W. (2020). Vision-Based Layout Detection from Scientific Literature using Recurrent Convolutional Neural Networks. Proceedings of the 25th International Conference on Pattern Recognition (ICPR 2020), Milan, Italy, January 10-15, 2021.

  29. Reinforcement Learning
    Deep Learning

    Behzadan, V. & Hsu, W. (2019). RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies. Proceedings of the 2nd International Workshop on Artificial Intelligence Safety Engineering (WAISE 2019), Turku, Finland, September 10, 2019.

  30. Reinforcement Learning
    Deep Learning

    Behzadan, V. & Hsu, W. (2019). Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Artificial Intelligence Safety (AISafety 2019), Macau, China, August 11, 2019.

  31. Data Mining

    Lamba, D., Alsadhan, M., Hsu, W., Fitzsimmons, E., & Newmark, G. (2019). Coping with Class Imbalance in Classification of Traffic Crash Severity Based on Sensor and Road Data: A Feature Selection and Data Augmentation Approach. In Nagamalai, D. et al., eds. Proceedings of the 6th International Conference on Data Mining and Database (DMDB 2019), pp. 125-137, Vancouver, BC, Canada, May 25-26, 2019.

  32. Natural Language Processing

    Bose, A., Behzadan, V., Aguirre, C., & Hsu, W. H. (2019). A Novel Approach for Detection and Ranking of Trendy and Emerging Cyber Threat Events in Twitter Streams. Proceedings of the Foundations of Open Source Intelligence and Security Informatics (FOSINT-SI 2019), Vancouver, Canada, August 27, 2019.

  33. Natural Language Processing
    Data Mining

    Yang, H., Aguirre, C. A., De La Torre, M. F., Christensen, D., Bobadilla, L., Davich, E., Roth, J., Luo, L., Theis, Y., Lam, A., Han, T. Y.-J., Buttler, D., & Hsu, W. H. (2019). Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science. Proceedings of the 2nd International Conference on Document Analysis and Recognition Workshop on Open Services and Tools for Document Analysis (ICDAR-OST 2019), Sydney, Australia, September 21, 2019.

  34. Data Mining
    High Performance Computing

    Tanash, M., Dunn, B., Andresen, D., Hsu, W., Yang, H., Okanlawon, A. (2019). Improving HPC System Performance by Predicting Job Resources via Supervised Machine Learning. Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC 2019), Chicago, IL, USA, July 28 - August 1, 2019.

  35. Natural Language Processing
    Data Mining

    Aguirre, C. A., Coen, S., De La Torre, M. F., Hsu, W. H., & Rys, M. (2018). Towards Faster Annotation Interfaces for Learning to Filter in Information Extraction and Search. Proceedings of the 2nd ACM Intelligent User Interfaces (IUI) Workshop on Exploratory Search and Interactive Data Analytics (ESIDA 2018), Tokyo, Japan, March 11, 2018.

  36. Computer Vision
    Deep Learning

    De La Torre, M. F., Hsu, W., & Cain, M. (2018). PawFriends: Small Vertebrate Motion Tracking and Behavioral Recognition from Video using LSTM. Working Notes of the 2nd International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Cognitive Vision: Integrated Vision and AI for Embodied Perception and Interaction (CogVis 2018), Stockholm, Sweden, July 14, 2018.

  37. Data Mining
    Network Science

    Kallumadi, S. & Hsu, W. H. (2018). Interactive Recommendations by Combining User-Item Preferences with Linked Open Data. Adjunct Publication of the 25th Conference on User Modeling, Adaptation, and Personalization (UMAP 2018), Nanyang Technological University, Singapore, July 8 - 11, 2018.

  38. Data Mining
    Machine Learning

    Yates, H., Chamberlain, B., Healey, J., & Hsu, W. (2018). Binary Classification of Arousal in Built Environments using Machine Learning. Working Notes of the 2nd International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Artificial Intelligence in Affective Computing, Stockholm, Sweden, July 15, 2018.

  39. Data Mining
    High Performance Computing

    Andresen, D., Hsu, W., Yang, H., & Okanlawon, A. (2018). Machine Learning for Predictive Analytics of Compute Cluster Jobs. Proceedings of the 16th International Conference on Scientific Computing (CSC 2018), Las Vegas, Nevada, USA, July 30 - August 2, 2018.

  40. Deep Learning
    Data Mining

    Behzadan, V., Aguirre, C., Bose, A., & Hsu, W. (2018). Corpus and Deep Learning Classifier for Collection of Cyber Threat Indicators in Twitter Stream. Proceedings of the IEEE International Conference on Big Data 2018 (IEEE BigData 2018) Workshop on Big Data Analytics for Cyber Threat Hunting (CyberHunt 2018), Seattle, WA, USA, December 10-13, 2018.

  41. Natural Language Processing
    Data Mining

    De La Torre, M. F., Aguirre, C. A., Anshutz, B., & Hsu, W. (2018). MATESC: Metadata-Analytic Text Extractor and Section Classifier for Scientific Publications. Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2018): International Conference on Knowledge Discovery and Information Retrieval (KDIR 2018), Seville, Spain, September 18-20, 2018.

  42. Data Mining
    Machine Learning

    Yates, H., Chamberlain, B., & Hsu, W. (2017). A Spatially Explicit Classification Model for Affective Computing in Built Environments. Proceedings of the 7th AAAC International Conference on Affective Computing and Intelligent Interaction (ACII 2017) Workshops and Demos, San Antonio, TX, USA, October 23-26, 2017.

  43. Natural Language Processing
    Data Mining

    Aguirre, C. A., Gullapalli, S., De La Torre, M. F., Lam, A., Weese, J. L., & Hsu, W. H. (2017). Learning to Filter Documents for Information Extraction using Rapid Annotation. Proceedings of the 1st International Conference on Machine Learning and Data Science (MLDS 2017), Noida, India, December 14-15, 2017.

  44. Data Mining
    Machine Learning

    Yates, H., Hsu, W., Chamberlain, B., & Norman, G. (2017). Arousal Detection for Biometric Data in Built Environments using Machine Learning. Working Notes of the 1st International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Artificial Intelligence in Affective Computing, Melbourne, Australia, August 20, 2017.

  45. Natural Language Processing
    Machine Learning

    Yang, M. & Hsu, W. H. (2016). HDPauthor: A New Hybrid Author-Topic Model using Latent Dirichlet Allocation and Hierarchical Dirichlet Processes. Proceedings of the 25th International World Wide Web Conference (WWW 2016) Workshop on Natural Language Processing for Informal Text (NLPIT 2016), pp. 619-624. Montréal, QC, Canada, April 12, 2016.

  46. Data Mining

    Weese, J. L. & Hsu, W. H. (2016). Work in Progress: Data Explorer – Assessment Data Integration, Analytics, and Visualization for STEM Education Research. Proceedings of the 123rd Annual American Society for Engineering Education Annual Conference and Exposition (ASEE 2016), New Orleans, LA, USA, June 26-29, 2016.

  47. Natural Language Processing
    Machine Learning

    Yang, M. & Hsu, W. H. (2015). Incorporation of Latent Dirichlet Allocation for Aspect-Level Sentiment into Hierarchical Dirichlet Process-Based Topic Models. Proceedings of the 7th Language and Technology Conference (LTC 2015): Human Language Technologies as a Challenge for Computer Science and Linguistics, in press. Poznań, Poland, November 27-29, 2015.

  48. Natural Language Processing
    Data Mining
    Health Medical

    Murphy, J. C., Hsu, W. H., Elshamy, W., Kallumadi, S. T., & Volkova, S. (2014). Greensickness and HPV: A Comparative Analysis? In Gniady, T., McAbee, K., & Murphy, J. C., eds., New Technologies in Renaissance Studies II volume 4, pp. 171-197. Toronto and Tempe, AZ, USA: Iter and Arizona Center for Medieval and Renaissance Studies.

  49. Natural Language Processing
    Data Mining
    Network Science
    Machine Learning

    Hsu, W. H., Koduru, P., & Zhai, C. (2013). Heterogeneous Information Networks for Text-Based Link Mining: A Position Paper on Visualization and Structure Learning Methods. Working Notes of the 2nd International Workshop on Heterogeneous Information Network Analysis (HINA 2013). Beijing, China.

  50. Natural Language Processing
    Data Mining

    Hsu, W. H., Abduljabbar, M., Osuga, R., Lu, M., & Elshamy, W. (2012). Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions. Proceedings of the 2nd European Workshop on Human-Computer Interaction and Information Retrieval (EuroHCIR 2012), pp. 43-46. Nijmegen, The Netherlands, August 24-25, 2012.

  51. Natural Language Processing
    Data Mining

    Hsu, W. H. & Koduru, P. (2012). Opinion Mapping: Information Visualization Approaches for Comparative Sentiment Analysis. Proceedings of the 2nd European Workshop on Human-Computer Interaction and Information Retrieval (EuroHCIR 2012), pp. 25-28. Nijmegen, The Netherlands, August 24-25, 2012.

  52. Data Mining
    Health Medical

    Volkova, S., & Hsu, W. H. (2010). Computational knowledge and information management in veterinary epidemiology. Proceedings of the 8th IEEE International Conference on Intelligence and Security Informatics (ISI 2010). Vancouver, BC, Canada, May 23-26, 2010.

  53. Data Mining
    Health Medical

    Volkova, S., Caragea, D., Hsu, W., & Bujuru, S. (2010). Animal disease event recognition and classification. Proceedings of the First International Workshop on Web Science and Information Exchange in the Medical Web (MedEx 2010). Raleigh, NC, USA, April 30, 2010.

  54. Natural Language Processing

    Weninger, T., Han, J., & Hsu, W. H. (2010). CETR - content extraction via tag ratios. In Proceedings of the 19th International World Wide Web Conference (WWW 2010). Raleigh, NC, USA, April 26-30, 2010.

  55. Natural Language Processing
    Data Mining

    Weninger, T., Ramachandran, S., Greene, D., Hart, J., Kancherlapalli, A., Hsu, W. H. & Han, J. (2010). Speech-Assisted Radiology System for Retrieval, Reporting and Annotation. Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, USA, July 25-28, 2010.

  56. Natural Language Processing
    Machine Learning

    Elshamy, W., Caragea, D., & Hsu, W. (2010). KSU KDD: Word sense induction by clustering in topic space. Proceedings of the Association for Computational Linguistics 5th International Workshop on Semantic Evaluation, pp. 367–370. Uppsala, Sweden, July, 2010.

  57. Probabilistic Reasoning
    Network Science

    Roy Chowdhury, S., Scoglio, C., & Hsu, W. H. (2010). Simulative modeling to control the foot and mouth disease epidemic. Proceedings of the International Conference on Computational Science (ICCS 2010) Workshop 27: Frontiers in the Computational Modeling of Disease Spreading. Procedia Computer Science, 2010(1), pp. 2261 - 2270. Amsterdam, The Netherlands, May 31 - June 2, 2010.

  58. Natural Language Processing
    Data Mining

    Volkova, S., Caragea, D., Hsu, W. H., Drouhard, J., & Fowles, L. (2010). Boosting Biomedical Entity Extraction by using Syntactic Patterns for Semantic Relation Discovery. Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2010), Toronto, ON, Canada, August 31 - September 3, 2010.

  59. Data Mining
    Network Science

    Xia, J., Caragea, D. and Hsu, W. (2009). Bi-relational network analysis using a fast random walk with restart. Proceedings of the IEEE International Conference on Data Mining (ICDM 2009). Miami, FL, USA, December 6-9, 2009.

  60. Network Science

    Weninger, T., Howell, R. R., & Hsu, W. H. (2009). Graph drawing heuristics for path finding in large dimensionless graphs. Proceedings of the 2009 International Conference on Artificial Intelligence (ICAI 2009). Las Vegas, NV, USA, July 13-16, 2009.

  61. Probabilistic Reasoning
    Machine Learning

    Xia, J., & Hsu, W. H. (2009). Protein protein interaction analysis using fast random walk. Proceedings of the 2009 International Conference on Artificial Intelligence (IC-AI 2009), pp. 857-863. Las Vegas, NV, USA, July 13-16, 2009.

  62. Data Mining
    Network Science

    Caragea, D., Bahirwani, V., Aljandal, W., & Hsu, W. H. (2009). Ontology-based link prediction in the LiveJournal social network. Proceedings of the 8th Symposium on Abstraction, Reformulation and Approximation (SARA 2009). Lake Arrowhead, CA, USA, July 7-10, 2009.

  63. Health Medical
    Speech Recognition

    Weninger, T., Greene, D., Hart, J., Hsu, W. H., & Ramachandran, S. (2009). Speech-assisted radiology system for retrieval, reporting and annotation, Proceedings of the 22nd IEEE Conference on Computer Based Medical Systems (CBMS-2009), Albuquerque, NM, USA, August 2-4, 2009.

  64. Probabilistic Reasoning
    Machine Learning

    Aljandal, W., Hsu, W. H., & Xia, J. (2009). Predicting Protein-Protein Interactions using Numerical Associational Features. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2009). Nashville, TN, USA, March 30 - April 2, 2009.

  65. Data Mining
    Network Science

    Aljandal, W., Hsu, W. H., Bahirwani, V., & Caragea, D. (2009) Ontology-Aware Classification and Association Rule Mining for Interest and Link prediction in Social Networks. Proceedings of the Association for the Advancement of Artificial Intelligence Spring Symposium on Social Semantic Web (AAAI SSW 2009), Stanford, CA, USA, March 23-25, 2009.

  66. Natural Language Processing
    Data Mining

    Weninger, T. & Hsu, W. H. Text Extraction from the Web via Text-To-Tag Ratio, Proceedings of the 19th International Conference on Database and Expert Systems Applications (DEXA 2008) Workshop on Text-based Information Retrieval. Turin, Italy, Sept 1-5, 2008.

  67. Data Mining
    Machine Learning

    Aljandal, W., Hsu, W. H., Bahirwani, V., Caragea, D., & Weninger, T. (2008). Validation-Based Normalization and Selection of Interestingness Measures for Association Rules. Proceedings of the 18th International Conference on Artificial Neural Networks in Engineering (ANNIE 2008). St. Louis, MO, USA, November 9-12, 2008.

  68. Data Mining
    Network Science

    Bahirwani, V., Caragea, D., Aljandal, W. & Hsu, W. H. (2008). Ontology Engineering and Feature Construction for Predicting Friendship Links in the Live Journal Social Network. Proceedings of the Second Workshop on Social Network Mining and Analysis, held in conjunction with the Association for Computing Machinery International Conference on Knowledge Discovery and Data Mining (SNA-KDD 2008). Las Vegas, NV, USA, August 24-27, 2008.

  69. Data Mining
    Network Science
    Machine Learning

    Hsu, W. H., Weninger, T., & Paradesi, M. S. R. (2008). Predicting Links and Link Change in Friends Networks - Supervised Time Series Learning with Imbalanced Data, Proceedings of the 18th International Conference on Artificial Neural Networks in Engineering (ANNIE 2008). St. Louis, MO, USA, November 9-12, 2008.

  70. Natural Language Processing
    Data Mining

    Weninger, T. & Hsu, W. H. (2008). Web Content Extraction Through Histogram Clustering. Proceedings of the 18th International Conference on Artificial Neural Networks in Engineering (ANNIE 2008). St. Louis, MO, USA, November 9-12, 2008.

  71. Probabilistic Reasoning
    Machine Learning

    Paradesi, M. S. R., Caragea, D., & Hsu, W. H. (2007). Structural Prediction of Protein-Protein Interactions in Saccharomyces cerevisiae. Proceedings of IEEE 7th International Symposium on BioInformatics and BioEngineering (BIBE 2007), pp. 1270-1274. Boston, MA, October, 2007.

  72. Data Mining
    Network Science
    Machine Learning

    Hsu, W. H., Lancaster, J. P., Paradesi, M. S. R., & Weninger, T. (2007). Structural Link Analysis from User Profiles and Friends Networks: A Feature Construction Approach, Proceedings of the 1st International Conference on Weblogs and Social Media (ICWSM 2007), pp. 75-80. Boulder, CO, USA, March, 2007.

  73. Data Mining
    Network Science

    Hsu, W. H., King, A. L., Paradesi, M., Pydimarri, T., & Weninger, T. (2006). Collaborative and Structural Recommendation of Friends using Weblog-based Social Network Analysis. Computational Approaches to Analyzing Weblogs - Papers from the 2006 Spring Symposium, Nicolov, N., Salvetti, F., Liberman, M., & Martin, J. H., eds. pp. 24-31. AAAI Press Technical Report SS-06-03. Stanford, CA, USA, March 27-29, 2006.

  74. Data Mining

    Paradesi, M. S. R., Wang, L., Brown, S. J., & Hsu, W. H. (2006). Mining Domain Association Rules From Protein-Protein Interaction data, Intelligent Engineering Systems through Artificial Neural Networks, vol. 16, pp. 213-218. St. Louis, MO, USA, November 2006.

  75. Genetic and Evolutionary Computation
    Machine Learning

    Koduru, P., Hsu, W. H., Das, S., Welch, S. M., & Roe, J. L. (2005). Dynamic Systems Prediction using Temporal Artificial Neural Networks and Multi-objective Genetic Algorithm. Proceedings of the International Association of Science and Technology for Development (IASTED) Conference on Computational Intelligence, pp. 214-219. Calgary, AB, Canada, July 4-6, 2005.

  76. Data Mining
    Probabilistic Reasoning

    Hsu, W. H. (2005). Relational Graphical Models for Collaborative Filtering and Recommendation of Computational Workflow Components. Working Notes of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-2005), Workshop 20: Multi-Agent Information Retrieval and Recommender Systems, pp. 18-22. Edinburgh, UK, July 31, 2005.

  77. Probabilistic Reasoning
    Machine Learning

    Guo, H. & Hsu, W. H. (2004). A Learning-Based Algorithm Selection Meta-Reasoner for the Real-Time MPE Problem. Proceedings of the 17th Australian Joint Conference on Artificial Intelligence (AI 2004), pp. 307-318. Cairns, QLD, Australia, December 4-6, 2004.

  78. Reinforcement Learning
    Genetic and Evolutionary Computation

    Harmon, S. J. , Rodríguez, E. , Zhong, C. A. & Hsu, W. H. (2004). Empirical Comparison of Incremental Learning Strategies for Genetic Programming-Based Keep-Away Soccer Agents. Proceedings of the AAAI 2004 Fall Symposium on Artificial Multi-Agent Learning. Washington, DC, USA, October 22-24, 2004.

  79. Probabilistic Reasoning

    Guo, H., Bodhireddy, P., & Hsu, W. H. (2004). An ACO Algorithm for the Most Probable Explanation Problem. Proceedings of the 17th Australian Joint Conference on Artificial Intelligence (AI 2004), pp. 778-790. Cairns, QLD, Australia, December 4-6, 2004.

  80. Probabilistic Reasoning
    Genetic and Evolutionary Computation
    Machine Learning

    Hsu, W. H. & Joehanes, R. (2004). Permutation Genetic Algorithms for Score-Based Bayesian Network Structure Learning. Proceedings of the International Conference on Computing, Communications and Control Technologies (CCCT 2004), pp. 273-280, Austin, TX, August 14-17, 2004.

  81. Reinforcement Learning
    Genetic and Evolutionary Computation

    Hsu, W. H. & Gustafson, S. M. (2002). Genetic Programming and Multi-Agent Layered Learning by Reinforcements. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 764 - 771. New York, NY, USA, July 9-13, 2002.

  82. Probabilistic Reasoning
    Genetic and Evolutionary Computation
    Machine Learning

    Hsu, W. H., Guo, H., Perry, B. B. & Stilson, J. A. (2002). A Permutation Genetic Algorithm for Variable Ordering in Learning Bayesian Networks from Data. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 383 - 390. New York, NY, USA, July 9-13, 2002.

  83. Reinforcement Learning
    Genetic and Evolutionary Computation

    Gustafson, S. M. & Hsu, W. H. (2001). Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. Proceedings of the 4th European Conference on Genetic Programming (EuroGP 2001), Springer LNCS 2038, pp. 291 - 301. Lake Como, Italy, April 18-20, 2001.

  84. Deep Learning
    Data Mining
    Machine Learning

    Hsu, W. H., Auvil, L. S., Pottenger, W. M., Tcheng, D., & Welge, M. (1999). Self-Organizing Systems for Knowledge Discovery in Databases. Proceedings of the International Joint Conference on Neural Networks (IJCNN 1999). Washington, DC, USA, July 10-16, 1999.

  85. Deep Learning
    Data Mining

    Hsu, W. H. & Ray, S. R. (1999). Construction of Recurrent Mixture Models for Time Series Classification. Proceedings of the International Joint Conference on Neural Networks (IJCNN 1999). Washington, DC, USA, July 10-16, 1999.

  86. Data Mining
    Genetic and Evolutionary Computation

    Hsu, W. H. Welge, M., Wu, J., & Yang, T. (1999). Genetic Algorithms for Selection and Partitioning of Attributes in Large-Scale Data Mining Problems. Freitas, A. A. (ed.), Data Mining with Evolutionary Algorithms: Research Directions - Papers from the Joint AAAI-GECCO Workshop, pp. 1 - 7, AAAI Press Technical Report WS-99-06. Orlando, FL, USA, July 18, 1999.

  87. Probabilistic Reasoning
    Machine Learning

    Hsu, W. H., Gettings, N. D., Lease, V. E., Pan, Y., & Wilkins, D. C. (1998). Heterogeneous Time Series Learning for Crisis Monitoring. Danyluk, A., Fawcett, T., & Provost, F. (eds.), Predicting the Future: AI Approaches to Time Series Problems - Papers from the Joint AAAI-ICML Workshop, pp. 34-41. AAAI Press Technical Report WS-98-07. Madison, WI, USA, July 26-30, 1998.

  88. Probabilistic Reasoning

    Grois, E., Hsu, W. H., Voloshin, M., & Wilkins, D. C. (1998). Bayesian Network Models for Automatic Generation of Crisis Management Training Scenarios. Proceedings of the 10th Innovative Applications of Artificial Intelligence Conference (IAAI 1998), pp. 1113-1120. Madison, WI, USA, July 26-30, 1998.

  89. Probabilistic Reasoning
    Machine Learning

    Hsu, W. H., Gettings, N. D., Lease, V. E., Pan, Y., & Wilkins, D. C. (1998). Crisis Monitoring: Methods for Heterogeneous Time Series Learning. Proceedings of the International Workshop on Multistrategy Learning (MSL 1998). Desenzano Del Garda, Italy, June 11-13, 1998.

  90. Probabilistic Reasoning
    Machine Learning

    Hsu, W. H. & Ray, S. R. (1998). Quantitative Model Selection for Heterogeneous Time Series. In Engels, R., Verdenius, F., & Aha, D. (eds.), The Methodology of Applying Machine Learning - Papers from the Joint AAAI-ICML Workshop, pp. 8-12. AAAI Press Technical Report WS-98-26. Madison, WI, USA, July 27, 1998.

  91. Deep Learning
    Machine Learning

    Hsu, W. H. & Ray, S. R. (1998). A New Mixture Model for Concept Learning From Time Series (Extended Abstract). Danyluk, A., Fawcett, T., & Provost, F. (eds.), Predicting the Future: AI Approaches to Time Series Problems - Papers from the Joint AAAI-ICML Workshop, pp. 42-43, AAAI Press Technical Report WS-98-07. Madison, WI, USA, July 27, 1998.

  92. Deep Learning
    Probabilistic Reasoning
    Machine Learning

    Hsu, W. H. (1997). A Position Paper on Statistical Inference Techniques which Integrate Bayesian and Stochastic Neural Network Models. Proceedings of the International Conference on Neural Networks (ICNN 1997), pp. 1972-1977. Houston, TX, USA, June 9-12, 1997.

  93. Probabilistic Reasoning
    Machine Learning

    Delcher, A., Kasif, S., Goldberg, H., & Hsu, W. (1993). Probabilistic Prediction of Protein Secondary Structure Using Causal Networks. Proceedings of the 11th National Conference on Artificial Intelligence (AAAI 1993), pp. 316-321. Washington, DC, USA, July 11-15, 1993.

  94. Probabilistic Reasoning
    Machine Learning

    Delcher, A., Kasif, S., Goldberg, H., & Hsu, W. (1993). Protein Secondary Structure Modelling with Probabilistic Networks. Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (ISMB 1993), pp. 109 - 117. Bethesda, MD, USA, July 6-9, 1993.