2016

Xiannian Fan. Scaling Up Bayesian Network Learning. PhD Dissertation. Computer Science, CUNY Graduate Center. 2016.

Xiaoyuan Zhu, Changhe Yuan. Exact Algorithms for MRE Inference. Journal of Artificial Intelligence Research, 55, pp.653-683. 2016.

Xiaoyuan Zhu, Changhe Yuan. Hierarchical Beam Search for Solving Most Relevant Explanation in Bayesian Networks. Journal of Applied Logic. 2016.

Cong Chen, Changhe Yuan, Chao Chen. Solving M-Modes Using Heuristic Search. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16). 2016.

2015

Changhe Yuan. Optimal Algorithms for Learning Bayesian Network Structures: Introduction and Heuristic Search. Tutorial. UAI 2015.

Arindam Khaled. Solving Influence Diagrams Using Branch and Bound Search. PhD Dissertation. Department of Computer Science and Engineering, Mississippi State University. 2015.

Xiannian Fan, Changhe Yuan. An Improved Lower Bound for Bayesian Network Structure Learning. In Proceedings of the 29th AAAI Conference (AAAI-15). Austin, Texas. 2015.

Xiaoyuan Zhu, Changhe Yuan. An Exact Algorithm for Solving Most Relevant Explanation in Bayesian Networks. In Proceedings of the 29th AAAI Conference (AAAI-15). Austin, Texas. 2015.

Xiaoyuan Zhu, Changhe Yuan. Hierarchical Beam Search for Solving Most Relevant Explanation in Bayesian Networks. In Proceedings of The 28th International FLAIRS Conference (FLAIRS-15). Hollywood, Florida. 2015. (Best Paper Award)

2014

Xiannian Fan, Brandon Malone, Changhe Yuan. Finding Optimal Bayesian Networks Using Constraints Learned from Data. In Proceedings of the 30th Annual Conference on Uncertainty in Artificial Intelligence (UAI-14). Quebec City, Quebec. 2014.

Ruilin Liu, Wendy Hui Wang , Changhe Yuan. Result Integrity Verification of Outsourced Bayesian Network Structure Learning. In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM-14), April 24 - 26, 2014, Philadephia, Pennsylvania, USA.

Xiannian Fan, Changhe Yuan, Brandon Malone. Tightening Bounds for Bayesian Network Structure Learning. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI-14). Quebec City, Quebec. 2014.

2013

James Cussens, Brandon Malone, Changhe Yuan. Tutorial on Optimal Algorithms for Learning Bayesian Networks. Tutorial. IJCAI 2013.

Changhe Yuan, Brandon Malone. Learning Optimal Bayesian Networks: A Shortest Path Perspective. Journal of Artificial Intelligence Research (JAIR). 2013.

Brandon Malone, Changhe Yuan. Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13). Seattle, Washington. 2013.

Arindam Khaled, Changhe Yuan, Eric Hansen. Solving Limited Memory Influence Diagrams Using Branch-and-Bound Search. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-13). Seattle, Washington. 2013.

Brandon Malone, Changhe Yuan. A Depth-first Branch and Bound Algorithm for Learning Optimal Bayesian Networks. IJCAI-13 Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR'13). Beijing, China. 2013.

2012

Changhe Yuan, Brandon Maone. An Improved Admissible Heuristic for Learning Optimal Bayesian Networks. In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-12). Catalina Island, CA. 2012.

Brandon Malone. Learning optimal Bayesian networks with heuristic search. PhD Dissertation. Department of Computer Science and Engineering, Mississippi State University. July, 2012.

Zhifa Liu, Changhe Yuan, Stephen Pruett. Machine Learning Analysis of the Relationship between Changes in Immunological Parameters and Changes in Resistance to Listeria monocytogenes: A New Approach for Risk Assessment and Systems Immunology. Toxicological sciences. 2012.

Zhifa Liu, Brandon Malone, Changhe Yuan. Empirical Evaluation of Scoring Functions for Bayesian Network Model Selection. BMC Bioinformatics. 2012.

Brandon Malone, Changhe Yuan. A Parallel, Anytime, Bounded Error Algorithm for Exact Bayesian Network Structure Learning. In Proceedings of the Sixth European Workshop on Probabilistic Graphical Models (PGM-12). Granada, Spain. 2012.

Arindam Khaled, Changhe Yuan, Eric Hansen. Solving Limited Memory Influence Diagrams Using Branch-and-Bound Search. In Proceedings of the International Symposium on Artificial Intelligence and Mathematics (ISAIM-12). Ft Lauderdale. 2012.

Changhe Yuan, Feng Cheng, Henry Dao, Markus Ettl, Grace Lin, Karthik Sourirajan. A Bayesian Framework for Supply Chain Risk Management Using Business Process Standards. Chapter in Handbook of Integrated Risk Management in Global Supply Chains. 2012

2011

Changhe Yuan, Heejin Lim, Tsai-Ching Lu. Most Relevant Explanation in Bayesian Networks. Journal of Artificial Intelligence Research (JAIR).

Changhe Yuan, Heejin Lim, Michael L. Littman. Most Relevant Explanation: Computational Complexity and Approximation Methods. Annals of Mathematics and Artificial Intelligence.

Changhe Yuan, Brandon Malone and Xiaojian Wu. Learning Optimal Bayesian Networks Using A* Search. 22nd International Joint Conference on Artificial Intelligence (IJCAI-11). Barcelona, Catalonia, Spain, July 2011.

Brandon Malone, Changhe Yuan, Eric Hansen and Susan Bridges. Memory-Efficient Dynamic Programming for Learning Optimal Bayesian Networks, 25th AAAI Conference on Artificial Intelligence (AAAI-11). San Francisco, CA. August 2011.

Brandon Malone, Changhe Yuan, Eric Hansen and Susan Bridges. Improving the Scalability of Optimal Bayesian Network Learning with Frontier Breadth-First Branch and Bound Search, 27th Conference on Uncertainty in Artificial Intelligence (UAI-11). Barcelona, Catalonia, Spain, July 2011.

2010

Changhe Yuan, Xiaojian Wu, Eric Hansen. Solving Multistage Influence Diagrams using Branch-and-Bound Search, In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI-10). July 8-11, 2010. Catalina Island, CA.

Nan Wang, Changhe Yuan, Dongfeng Wu, Shane Burgess, Susan Bridges. PepOut: Distance-based Outlier Detection Model for Improving MS/MS Peptide Identification Confidence. International Journal of Data Mining and Bioinformatics.

Heejin Lim, Changhe Yuan, Eric Hansen. Scaling Up MAP Search in Bayesian Networks Using External Memory, In Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (PGM-10). September 13-15, 2010. Helsinki, Finland.

Xiaojian Wu. A Heuristic Search Algorithm for Learning Optimal Bayesian Networks. M.S. Thesis. Department of Computer Science and Engineering, Mississippi State University. 2010.

Heejin Lim, Changhe Yuan. Computational complexity and approximation methods of most relevant explanation, In Proceedings of the Eleventh International Symposium on Artificial Intelligence and Mathematics (ISAIM-10). January 6-8, 2010. Ft Lauderdale, FL.

Changhe Yuan, Xiaojian Wu. Solving influence diagrams using heuristic search, In Proceedings of the Eleventh International Symposium on Artificial Intelligence and Mathematics (ISAIM-10). January 6-8, 2010. Ft Lauderdale, FL.

2009

Changhe Yuan, Xiaolu Liu, Tsai-Ching Lu, Heejin Lim, Most Relevant Explanation: Properties, Algorithms, and Evaluations, In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI-09), June 18-21, Montreal, Canada. (Acceptance rate: poster, 31%)

Changhe Yuan, Eric Hansen, Efficient Computation of Jointree Bounds for Systematic MAP Search. In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09). Pasadena, CA. 2009. (Acceptance rate: oral, 25%)

Changhe Yuan, Some Properties of Most Relevant Explanation, In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence ExaCt Workshop (ExaCt-09), Pasadena, CA. 2009.

Feng Cheng, Henry H. Dao, Markus Ettl, Mary E. Helander, Jayant Kalagnanam, Karthik Sourirajan, Changhe Yuan. Method and System for Business Process Oriented Risk Identification and Qualification. US Patent application. IBM ref.# YOR920080535US1.

2008

Changhe Yuan, Tsai-Ching Lu, A General Framework for Generating Multivariate Explanations in Bayesian Networks. In Proceedings of the Twenty-Third National Conference on Artificial Intelligence (AAAI-08). (Acceptance rate: oral, 24%)

Changhe Yuan, Eric Hansen, MAP Search in Bayesian Networks Using Joint Bounds. In Proceedings of AAAI-08 workshop on Search for Artificial Intelligence and Robotics. (Acceptance rate: oral, 43%)

Nan Wang, Changhe Yuan, Shane Burgess, Bindu Nanduri, Mark Lawrence, Susan Bridges, Integrating evidence for evaluation of potential novel protein-coding genes using Bayesian networks. In Proceedings of the 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP-08).

2007

Changhe Yuan, Marek J. Druzdzel, Theoretical Analysis and Practical Insights into Importance Sampling for Bayesian Networks. International Journal of Approximate Reasoning. Vol. 46, Pages 320-333, 2007.

Changhe Yuan, and Marek J. Druzdzel, Generalized Evidence Pre-propagated Importance Sampling for Hybrid Bayesian Networks. In Proceedings of the Twenty-second National Conference on Artificial Intelligence (AAAI-07). (Acceptance rate: oral&poster, 5.1%)

Xiaoxun Sun, Marek J. Druzdzel, Changhe Yuan, Dynamic Weighting A* Search-Based MAP Algorithm for Bayesian Networks. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-07). Pages 2385-2390. 2007. (Acceptance rate: oral, 15.7%)

Changhe Yuan, and Marek J. Druzdzel, Importance Sampling for General Hybrid Bayesian Networks. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics (AISTAT-07).

Changhe Yuan, and Tsai-Ching Lu, Finding Explanations in Bayesian Networks. In Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07).

Changhe Yuan, and Marek J. Durzadzel, Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks. In Proceedings of the Twentieth Canadian Conference on Artificial Intelligence (AI-07). (Acceptance rate: oral, 17%)

2006

Changhe Yuan, Importance Sampling for Bayesian Networks: Principles, Algorithms, and Performance. Ph.D. Dissertation, Intelligent Systems Program, University of Pittsburgh, 2006.

Changhe Yuan, Marek J. Druzdzel, Importance Sampling Algorithms for Bayesian Networks: Principles and Performance. Mathematical and Computer Modeling, Vol. 43, Pages 1189-1207, 2006.

Xiaoxun Sun, Marek J. Druzdzel, Changhe Yuan, Dynamic Weighting A* Search-Based MAP Algorithm for Bayesian Networks. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 279-286, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.

Changhe Yuan, Marek J. Druzdzel, Hybrid Loopy Belief Propagation. In Proceedings of the Third European Workshop on Probabilistic Graphical Models (PGM-06), pages 317-324, Milan Studeny and Jiri Vomlel (eds.), Prague: Action M Agency, 2006.

2005 and Prior

Changhe Yuan, Marek J. Druzdzel, Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions. In Proceedings of the 21st Annual Conference on Uncertainty in Artificial Intelligence (UAI-05). Pages 650-657. July 2005. (Acceptance rate: poster, 34%)

Changhe Yuan, Marek J. Druzdzel, How Heavy Should the Tails Be? In Proceedings of the 18th International Florida Artificial Intelligence Research Society Conference (FLAIRS-05). Pages 799-805. May 2005. (Acceptance rate: oral 53%)

Changhe Yuan, Tsai-Ching Lu, Marek J. Druzdzel, Annealed MAP. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04). Pages 628-635. July 2004. (Acceptance rate: oral, 10.4%)

Changhe Yuan, Marek J. Druzdzel, A Comparison on the Effectiveness of Two Heuristics for Importance Sampling. In Peter Lucas (Ed.): Proceedings of the Second European Workshop on Probabilistic Graphical Models (PGM-04), Leiden, October 2004, Pages 225-232.

Changhe Yuan, Marek J. Druzdzel, An Importance Sampling Algorithm Based on Evidence Pre-propagation. In Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03). Pages 624-631. August 2003. (Acceptance rate: poster 23%)

Changhe Yuan, EPIS-BN: An Importance Sampling Algorithm Based on Evidence Pre-propagation. M.S. Thesis, Intelligent Systems Program, University of Pittsburgh, 2003.

Changhe Yuan, Research and Development of Rough Set Theory-based Data Mining Technology. M.S. Thesis, Computer Science Department, Tongji University, 2001.

Changhe Yuan, Yongming Wu, Research and Development of Decision Support Systems Based on Data Warehouse, Computer Engineering and Application, 37(16), 2001.