Other (2)

• Marling, C., Struble, N., Bunescu, R., Shubrook, J., Schwartz, F. (2013). A Consensus Perceived Glycemic Variability Metric.
• Marling, C., Struble, N., Bunescu, R., Shubrook, J., Schwartz, F. (2012). A Consensus Perceived Glycemic Variability Metric. Bethesda, Maryland: Diabetes Technology Meeting.

Technical Report (2)

• Katebi, R., Zhou, Y., Chornock, R., Bunescu, R. (2018). Galaxy morphology prediction using capsule networks. https://arxiv.org/abs/1809.08377.
• Bagnall, A., Bunescu, R., Stewart, G. (2017). Training Ensembles to Detect Adversarial Examples. http://arxiv.org/abs/1712.04006.

Conference Proceeding (47)

• Ye, X., Fang, F., Wu, J., Bunescu, R., Liu, C. (2018). Bug Report Classification using LSTM architecture for More Accurate Software Defect Locating. 2018 Special Session on Machine Learning for Predictive Models in Engineering Applications (MLPMEA 2018).
• Chen, C., Kim, S., Bui, H., Rossi, R., Koh, E., Kveton, B., Bunescu, R. (2018). Predictive Analysis by Leveraging Temporal User Behavior and User Embeddings. Turin: The 27th ACM International Conference on Information and Knowledge Management (CIKM); http://ace.cs.ohio.edu/~razvan/papers/cikm18.pdf.
• Marling, C., Bunescu, R. (2018). The OhioT1DM dataset for Blood Glucose Level Prediction. Stockholm: IJCAI workshop on Knowledge Discovery in Healthcare Data (KDH); http://ceur-ws.org/Vol-2148/paper09.pdf.
• Clark, M., Kodi, A., Bunescu, R., Louriu, A. (2018). LEAD: Learning-enabled Energy-Aware Dynamic Voltage/Frequency Scaling in NoCs. San Francisco: The 55th Annual Design Automation Conference (DAC); http://ace.cs.ohio.edu/~razvan/papers/dac18.pdf.
• Van Winkle, S., Kodi, A., Bunescu, R., Louri, A. (2018). Extending the Power-Efficiency and Performance of Photonic Interconnects for Heterogeneous Multicores with Machine Learning. Vienna: The 24th IEEE International Symposium on High-Performance Computer Architecture (HPCA); http://ace.cs.ohio.edu/~razvan/papers/hpca18.pdf.
• Naguri, C., Bunescu, R. (2017). Recognition of Dynamic Hand Gestures from 3D Motion Data using LSTM and CNN architectures. IEEE International Conference on Machine Learning and Applications ; 4. http://ace.cs.ohiou.edu/~razvan/papers/icmla17.pdf.
• Chen, L., Bunescu, R. (2017). An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community QA. International Joint Conference on Natural Language Processing (IJCNLP); 442-447. http://aclweb.org/anthology/I17-2075.
• Masada, K., Bunescu, R. (2017). Chord Recognition in Symbolic Music using semi-Markov Conditional Random Fields. International Society for Music Information Retrieval (ISMIR); 272-278. https://ismir2017.smcnus.org/wp-content/uploads/2017/10/224_Paper.pdf.
• Mirshekarian, S., Bunescu, R., Marling, C., Schwartz, F. (2017). Using LSTMs to Learn Physiological Models of Blood Glucose Behavior. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2887-2891. http://ieeexplore.ieee.org/document/8037460/.
• Chen, C., Bunescu, R., Xu, L., Liu, C. (2016). Tone Classification in Mandarin Chinese using Convolutional Neural Networks. San Francisco, CA: International Speech Communication Association (ISCA) Conference (Interspeech); http://www.isca-speech.org/archive/Interspeech_2016/pdfs/0528.PDF.
• Gray, P., Bunescu, R. (2016). A Neural Greedy Model for Voice Separation in Symbolic Music . New York, NY: International Symposium on Music Information Retrieval; 782-788. https://wp.nyu.edu/ismir2016/wp-content/uploads/sites/2294/2016/07/296_Paper.pdf.
• Marling, C., Xia, L., Bunescu, R., Schwartz, F. (2016). Machine Learning Experiments with Noninvasive Sensors for Hypoglycemia Detection. New York, New York: http://sites.google.com/site/ijcai2016kdhealth/accepted-papers.
• Ye, X., Shen, H., Ma, X., Bunescu, R., Liu, C. (2016). From Word Embeddings To Document Similarities for Improved Information Retrieval in Software Engineering. Austin, TX: ACM/IEEE International Conference on Software Engineering (ICSE); 404-415 . http://dl.acm.org/citation.cfm?id=2884862.
• Marling, C., Bunescu, R., Baradar-Bokaie, B., Schwartz, F. (2015). Case-Based Reasoning as a Prelude to Big Data Analysis: A Case Study. Frankfurt, Germany: 175-183.
• Ye, X., Bunescu, R., Liu, C. (2014). Learning to Rank Relevant Files for Bug Reports using Domain Knowledge. 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE); 11. http://dl.acm.org/citation.cfm?id=2635874.
• Plis, K., Bunescu, R., Marling, C., Shubrook, J., Schwartz, F. (2014). A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management . Palo Alto, CA: AAAI Press; 35-39.
• Bunescu, R., Struble, N., Marling, C., Shubrook, J., Schwartz, F. (2013). Blood Glucose Level Prediction using Physiological Models and Support Vector Regression. International Conference on Machine Learning and Applications (ICMLA) 2013; http://www.icmla-conference.org/icmla13/.
• Dandala, B., Mihalcea, R., Bunescu, R. (2013). Multilingual Word Sense Disambiguation Using Wikipedia. Asian Federation of Natural Language Processing; http://aclweb.org/anthology//I/I13/I13-1000.pdf.
• Dandala, B., Hokamp, C., Mihalcea, R., Bunescu, R. (2013). Sense Clustering Using Wikipedia. Proceedings of RANLP; http://lml.bas.bg/ranlp2013/proceedings.php.
• Shen, H., Bunescu, R., Mihalcea, R. (2013). Coarse to Fine Grained Sense Disambiguation in Wikipedia. Association for Computational Linguistics; http://aclweb.org/anthology//S/S13/S13-1003.pdf.
• Shen, H., Bunescu, R., Mihalcea, R. (2012). Sense and Reference Disambiguation in Wikipedia. Mumbai: 24th International Conference on Computational Linguistics; 1111-1120. http://www.aclweb.org/anthology/C12-2108.
• Marling, C., Bunescu, R., Shubrook, J., Schwartz, F. (2012). System Overview: The 4 Diabetes Support System. Lyon: Workshop Proceedings of the Twentieth International Conference on Case-Based Reasoning; 81-86.
• Bunescu, R. (2012). An Adaptive Clustering Model that Integrates Expert Rules and N-gram Statistics for Coreference Resolution. Montpellier: 20th European Conference on Artificial Intelligence; 242: 897-898. http://www.booksonline.iospress.nl/Content/View.aspx?piid=31730.
• Bunescu, R. (2012). Adaptive Clustering for Coreference Resolution with Deterministic Rules and Web-Based Language Models. Stroudsburg, PA: First Joint Conference on Lexical and Computational Semantics; 1: 11--19. http://dl.acm.org/citation.cfm?id=2387639.
• Dandala, B., Mihalcea, R., Bunescu, R. (2012). Towards building a multilingual semantic network: identifying interlingual links in Wikipedia. Stroudsburg, PA: First Joint Conference on Lexical and Computational Semantics; 1: 30-37. http://dl.acm.org/citation.cfm?id=2387641.
• Marling, C., Wiley, M., Cooper, T., Bunescu, R., Shubrook, J., Schwartz, F. (2011). "The 4 Diabetes Support System: A Case Study in CBR Research and Development," in Case-Based Reasoning Research and Development: 19th International Conference on Case-Based Reasoning, ICCBR 2011 Proceedings. Berlin: Springer.
• Wiley, M., Bunescu, R., Marling, C., Shubrook, J., Schwartz, F. (2011). Automatic Detection of Excessive Glycemic Variability for Diabetes Management. Honolulu, Hawaii: The 10th International Conference on Machine Learning Applications; http://www.icmla-conference.org/icmla11/index.htm.
• Marling, C., Wiley, M., Bunescu, R., Shubrook, J., Schwartz, F. (2011). Emerging Applications for Intelligent Diabetes Management. San Francisco, CA: The 23rd Annual Conference on Innovative Applications of Artificial Intelligence (IAAI); 1668-1673. http://www.aaai.org/ocs/index.php/IAAI/IAAI-11.
• Mohler, M., Bunescu, R., Rada, M. (2011). Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments. Portland, OR: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT); 753-762. http://www.acl2011.org/.
• Bunescu, R., Huang, Y. (2010). Learning the Relative Usefulness of Questions in Community QA. Cambridge, MA: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP); 97-107. http://www.aclweb.org/anthology-new/D/D10/D10-1010.pdf.
• Bunescu, R., Yunfeng, H. (2010). A Utility-Driven Approach to Question Ranking. Beijing: Proceedings of the 23rd International Conference on Computational Linguistics (COLING); 125-133. http://aclweb.org/anthology-new/C/C10/C10-1015.pdf.
• Bunescu, R. (2008). Learning with Probabilistic Features for Improved Pipeline Models. Waikiki, Honolulu, Hawaii: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).
• Bunescu, R., Mooney, R. (2007). Learning to Extract Relations from the Web using Minimal Supervision. Prague, Czech Republic: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL).
• Bunescu, R., Mooney, R. (2007). Multiple Instance Learning for Sparse Positive Bags. Corvallis, OR: Proceedings of the 24th International Conference on Machine Learning (ICML).
• Bunescu, R., Mooney, R., Ramani, A., Marcotte, E. (2006). Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline. New York City, NY: Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology: Towards deeper biological literature analysis (BioNLP-2006); 49-56.
• Bunescu, R., Pasca, M. (2006). Using Encyclopedic Knowledge for Named Entity Disambiguation. Trento, Italy: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL).
• Bunescu, R., Mooney, R. (2005). Subsequence Kernels for Relation Extraction. Vancouver, BC: Proceedings of the 19th Conference on Neural Information Processing Systems (NIPS).
• Bunescu, R., Mooney, R. (2005). A Shortest Path Dependency Kernel for Relation Extraction. Vancouver, BC: Proceedings of the Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP).
• Ramani, A., Marcotta, E., Bunescu, R., Mooney, R. (2005). Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions. Detroit, MI: Proceedings of the ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics,; 46--53.
• Bunescu, R., Mooney, R. (2004). Collective Information Extraction with Relational Markov Networks. Barcelona, Spain: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL).
• Bunescu, R., Mooney, R. (2004). Relational Markov Networks for Collective Information Extraction. Banff, Canada: Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004).
• Yi, J., Nasukawa, T., Bunescu, R., Niblack, W. (2003). Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques. Melbourne, Florida: Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM).
• Bunescu, R., Ge, R., Kate, R., Marcotte, E., Mooney, R., Ramani, A., Wong, Y. (2003). Learning to Extract Proteins and their Interactions from Medline Abstracts. Washington DC: Proceedings of the ICML-2003 Workshop on Machine Learning in Bioinformatics; 46-53.
• Bunescu, R. (2003). Associative Anaphora Resolution: A Web-Based Approach. Budapest, Hungary: Proceedings of the EACL 2003 Workshop on The Computational Treatment of Anaphora.
• Harabagiu, S., Moldovan, D., Pasca, M., Mihalcea, R., Surdeanu, M., Bunescu, R., Girju, R., Rus, V., Morarescu, P. (2001). The Role of Lexico-Semantic Feedback in Open-Domain Textual Question-Answering. Toulouse, France: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL).
• Harabagiu, S., Bunescu, R., Maiorano, S. (2001). Text and Knowledge Mining for Coreference Resolution. Pittsburgh, PA: Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL).
• Harabagiu, S., Moldovan, D., Pasca, M., Mihalcea, R., Surdeanu, M., Bunescu, R., Girju, R., Rus, V., Morarescu, P. (2000). FALCON: Boosting Knowledge for Answer Engines. Gaithersburg, Maryland: Proceedings of the 9th Text REtrieval Conference (TREC 2000).

Journal Article, Professional Journal (1)

• Bunescu, R., Huang, Y. (2010). Towards a General Model of Answer Typing: Question Focus Identification. Mexico City: Research in Computing Science: Special Issue on Natural Language Processing and Applications; 46: 231--242. http://www.cicling.org/2010/Vol46.pdf, www.cicling.org/micai/rcs/.

Journal Article, Academic Journal (10)

• Masada, K., Bunescu, R. (2018). Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music. 1. 2: 1-13. https://transactions.ismir.net/article/10.5334/tismir.18/.
• Fettes, Q., Clark, M., Bunescu, R., Kodi, A., Louri, A. (2018). Dynamic Voltage and Frequency Scaling in NoCs with Supervised and Reinforcement Learning Techniques. IEEE Transactions on Computers; http://ace.cs.ohio.edu/~razvan/papers/draft-tc18.pdf.
• Schwartz, F., Marling, C., Bunescu, R. (2018). The Promise and Perils of Wearable Physiological Sensors for Diabetes Management. Journal of Diabetes Science and Technology (JDST); http://journals.sagepub.com/doi/abs/10.1177/1932296818763228.
• DiTomaso, D., Kodi, A., Louri, A., Bunescu, R. (2015). Resilient and Power-Efficient Multi-Function Channel Buffers in Network-on-Chip Architectures. 12. IEEE Transactions on Computers; 64: 3555-3568. http://doi.ieeecomputersociety.org/10.1109/TC.2015.2401013.
• Ye, X., Bunescu, R., Liu, C. (2015). Mapping Bug Reports to Relevant Files: A Ranking Model, a Fine-grained Benchmark, and Feature Evaluation. IEEE Transactions on Software Engineering; 99: 26. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7270328.
• Marling, C., Struble, N., Bunescu, R., Shubrook, J., Schwartz, F. (2013). A Consensus Perceived Glycemic Variability Metric. 4. Journal of Diabetes Science and Technology; 7: 871-879.
• Marling, C., Bunescu, R., Shubrook, J., Schwartz, F. (2012). Emerging Applications for Intelligent Diabetes Management. 2. AI Magazine; 33: 67-78.
• Bunescu, R., Ge, R., Kate, R., Marcotte, E., Mooney, R., Ramani, A., Wong, Y. (2005). Comparative Experiments on Learning Information Extractors for Proteins and their Interactions. 2. Artificial Intelligence in Medicine (Special Issue on Summarization and Information Extraction from Medical Documents); 33: 139-155.
• Ramani, A., Bunescu, R., Mooney, R., Marcotte, E. (2005). Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome. 5, r40. Genome Biology; 6.
• Mooney, R., Bunescu, R. (2005). Mining Knowledge from Text Using Information Extraction. 1. SIGKDD Explorations (Special Issue on Text Mining and Natural Language Processing); 7: 3-10.

Book, Chapter in Scholarly Book (3)

• Dandala, B., Mihalcea, R., Bunescu, R. (2013). Word Sense Disambiguation using Wikipedia. The People’s Web Meets NLP: Collaboratively Constructed Language Resources; 23. http://www.springer.com/education+%26+language/linguistics/book/978-3-642-35084-9.
• Bunescu, R., Mooney, R. (2007). “Extracting Relations from Text: From Word Sequences to Dependency Paths” in Text Mining and Natural Language Processing. Springer; 29–44.
• Bunescu, R., Mooney, R. (2007). “Statistical Relational Learning for Natural Language Information Extraction” in Statistical Relational Learning. Cambridge, MA: MIT Press; 535-552.

Conference, Poster (1)

• Chen, L., Bunescu, R., Xu, L., Liu, C. (2016). Mandarin tone recognition based on unsupervised feature learning from spectrograms . The 5th Joint Meeting of the Acoustical Society of America and the Acoustical Society of Japan.