Probabilistic Reasoning Group Publications

Publications

Journals

[HWRC02] W. H. Hsu, M. Welge, T. Redman, and D. Clutter. Constructive Induction Wrappers in High-Performance Commercial Data Mining and Decision Support Systems. Knowledge Discovery and Data Mining. Kluwer Academic Publishers, to appear. (PostScript .ps.gz)
[HRW00] W. H. Hsu, S. R. Ray, and D. C. Wilkins. A Multistrategy Approach to Classifier Learning from Time Series. Machine Learning, 38(1-2):213-236. Kluwer Academic Publishers, 2000. (PostScript .ps.gz)
[RH98] S. R. Ray and W. H. Hsu. Self-Organized-Expert Modular Network for Classification of Spatiotemporal Sequences. Intelligent Data Analysis, 2(4). IOS Press, October, 1998. (PostScript .ps.gz)
[HZ95] W. H. Hsu and A. E. Zwarico. Automatic Synthesis of Compression Techniques for Heterogeneous Files. Software: Practice and Experience, 25(10):1097-1116. Wiley, 1995. (PostScript .ps.gz)

Books

[Hs02] W. H. Hsu. Control of Inductive Bias in Supervised Learning using Evolutionary Computation: A Wrapper-Based Approach. In J. Wang, editor, Data Mining: Opportunities and Challenges. IDEA Group Publishing, to appear. (PostScript .ps.gz)

Conferences

[Gu02] H. Guo. A Bayesian Metareasoner for Algorithm Selection for Real-time Bayesian Network Inference Problems. AAAI02 Doctoral Consortium Abstract, to appear.
[GPSH02] H. Guo, B. B. Perry, J. A. Stilson, W. H. Hsu. A Genetic Algorithm for Tuning Variable Orderings in Bayesian Network Structure Learning. AAAI02 Student Abstract, to appear.
[DGVH02] S. Das, S. Gosavi, S. Vaze, and W. H. Hsu. An Ant Colony Approach for the Steiner Tree Problem (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002, to appear. (PostScript .ps.gz)
[HG02] W. H. Hsu and S. M. Gustafson. Genetic Programming and Multi-Agent Layered Learning by Reinforcements. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002, to appear. (PostScript .ps.gz)
[HGPS02] W. H. Hsu, H. Guo, B. B. Perry, and J. A. Stilson. A Permutation Genetic Algorithm for Variable Ordering in Learning Bayesian Networks from Data. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002, to appear. (PostScript .ps.gz)
[HSL02] W. H. Hsu, C. P. Schmidt, and J. A. Louis. Genetic Algorithm Wrappers for Feature Subset Selection in Supervised Inductive Learning (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002, to appear. (PostScript .ps.gz)
[GH01] S. M. Gustafson and W. H. Hsu. Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem. In Proceedings of the 4th European Conference on Genetic Programming (EuroGP-2001), Lake Como (Milan), Italy, April, 2001. Springer-Verlag, 2001. (PostScript .ps.gz)
[HWRC00] W. H. Hsu, M. Welge, T. Redman, and D. Clutter. Genetic Wrappers for Constructive Induction in High-Performance Data Mining (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, NV, July, 2000. Morgan Kaufmann Publishers, San Mateo, CA, 2000. (PostScript .ps.gz)
[HCGG00] W. H. Hsu, Y. Cheng, H. Guo, and S. Gustafson. Genetic Algorithms for Reformulation of Large-Scale KDD Problems with Many Irrelevant Attributes (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), Las Vegas, NV, July, 2000. Morgan Kaufmann Publishers, San Mateo, CA, 2000. (PostScript .ps.gz)
[GH00] S. M. Gustafson and W. H. Hsu. Genetic Programming for Strategy Learning in Soccer-Playing Agents: A KDD-Based Architecture. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000) Workshop Program, Las Vegas, NV, July, 2000. (PostScript .ps.gz)
[HAR+99] W. H. Hsu, L. S. Auvil, T. Redman, D. Tcheng, and M. Welge. High-Performance Knowledge Discovery and Data Mining Systems Using Workstation Clusters (poster abstract). Presented at National Conference on High Performance Networking and Computing (SC99), Portland, OR, November, 1999. (PostScript .ps.gz)
[HAP+99] W. H. Hsu, L. S. Auvil, W. M. Pottenger, D. Tcheng, and M. Welge. Self-Organizing Systems for Knowledge Discovery in Databases. In Proceedings of the International Joint Conference on Neural Networks (IJCNN-99), Washington, DC, July, 1999. (PostScript .ps.gz)
[HR99] W. H. Hsu and S. R. Ray. Construction of Recurrent Mixture Models for Time Series Classification. In Proceedings of the International Joint Conference on Neural Networks (IJCNN-99), Washington, DC, July, 1999. (PostScript .ps.gz)
[HWWY99a] W. H. Hsu, M. Welge, J. Wu, and T. Yang. Genetic Algorithms for Selection and Partitioning of Attributes in Large-Scale Data Mining Problems. In Proceedings of the Joint AAAI-GECCO Workshop on Data Mining with Evolutionary Algorithms, Orlando, FL, July, 1999. (PostScript .ps.gz)
[HWWY99b] W. H. Hsu, M. Welge, J. Wu, and T. Yang. Genetic Algorithms for Synthesis of Attributes in Large-Scale Data Mining (poster abstract). In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Orlando, FL, July, 1999. (PostScript .ps.gz)
[GHVW98] E. Grois, W. H. Hsu, M. Voloshin, and D. C. Wilkins. Bayesian Network Models for Automatic Generation of Crisis Management Training Scenarios. In Proceedings of the Tenth Innovative Applications of Artificial Intelligence Conference (IAAI-98), pp. 1113-1120. Madison, WI, July, 1998. (PostScript .ps.gz)
[HGL+98a] W. H. Hsu, N. D. Gettings, V. E. Lease, Y. Pan, and D. C. Wilkins. Crisis Monitoring: Methods for Heterogeneous Time Series Learning. In Proceedings of the International Workshop on Multistrategy Learning (MSL-98). Milan, Italy, June, 1998. (PostScript .ps.gz)
[HGL+98b] W. H. Hsu, N. D. Gettings, V. E. Lease, Y. Pan, and D. C. Wilkins. Heterogeneous Time Series Learning for Crisis Monitoring. In A. Danyluk, T. Fawcett, and F. Provost, editors, Proceedings of the Joint AAAI-ICML Workshop on AI Approaches to Time Series Problems, pp. 34-41. Madison, WI, July, 1998. (PostScript .ps.gz)
[HR98a] W. H. Hsu and S. R. Ray. A New Mixture Model for Concept Learning From Time Series (Extended Abstract). In A. Danyluk, T. Fawcett, and F. Provost, editors, Proceedings of the Joint AAAI-ICML Workshop on AI Approaches to Time Series Problems, pp. 42-43. Madison, WI, July, 1998. (PostScript .ps.gz)
[HR98b] W. H. Hsu and S. R. Ray. Quantitative Model Selection for Heterogeneous Time Series. In R. Engels, F. Verdenius, and D. Aha, editors, Proceedings of the Joint AAAI-ICML Workshop on the Methodology of Applying Machine Learning, pp. 8-12. Madison, WI, July, 1998. (PostScript .ps.gz)
[Hs97a] W. H. Hsu. A Position Paper on Statistical Inference Techniques Which Integrate Bayesian and Stochastic Neural Network Models. In Proceedings of the International Conference on Neural Networks (ICNN-97), pp. 1972-1977. Houston, TX, June, 1997. (PostScript, no figures .ps.gz, no figures)
[Hs97b] W. H. Hsu. Probabilistic Learning in Bayesian and Stochastic Neural Networks (Doctoral Consortium Abstract). In Proceedings of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), p. 810. Providence, RI, July, 1997. (PostScript .ps.gz)
[DKGH93a] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Probabilistic Prediction of Protein Secondary Structure Using Causal Networks. In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp. 316-321. Washington, DC, August, 1993.
[DKGH93b] A. Delcher, S. Kasif, H. Goldberg, W. Hsu. Prediction of Protein Secondary Fold Using Probabilistic Networks. In Proceedings of the First International Conference on Intelligent Systems for Molecular Biology (ISMB-93). Bethesda, MD, July, 1993.

Books

[HWRC00] W. H. Hsu, M. Welge, T. Redman, and D. Clutter. High-Performance Commercial Data Mining: A Multistrategy Machine Learning Application. National Center for Supercomputing Applications Technical Report NCSA-ALG-2000-01. Automated Learning Group (ALG), National Center for Supercomputing Applications (NCSA), UIUC, 2000.
[Hs98] W. H. Hsu. Time Series Learning With Probabilistic Network Composites. Ph.D. thesis, University of Illinois at Urbana-Champaign (Technical Report UIUC-DCS-R2063). August, 1998. (PDF PostScript .ps.gz)
[WFH+96] D. C. Wilkins, C. Fagerlin, W. H. Hsu, E. T. Lin, and D. Kruse. Design of a Damage Control Simulator. Knowledge Based Systems Laboratory Technical Report UIUC-BI-KBS-96005. Beckman Institute, UIUC, 1996.

Bioinformatics and Medical Informatics References

(Last updated 25 Feb 2002)

[ARRT00] A. Zien, R. Kuffner, R. Zimmer, T. Lenguaer (2000) Analysis of gene expression data with pathway scores . In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. AAAI Press. pdf
[BF01] Y. Barash and N. Friedman (2001, April). Context-specific bayesian clustering for gene expression data. In T. Lengauer et al. (Eds.), RECOMB 2001, Proceedings of the Fifth Annual International Conference on Computational Biology, Montreal, Canada, pp. 12-21. pdf
[BFGK96] C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller (1996) Context-specific independence in bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), pp. 115-123. pdf
[BSY99] Amir Ben-Dor, Ron Shamir, and Zohar Yakhini (1999) Clustering gene expression patterns. In Proceedings of the Third Annual International Conference on Computational Molecular Biology (RECOMB'99, Lyon, France, Aug. 11-14, 1999) pdf
[Bu91] W. Buntine (1991) Theory refinement on Bayesian networks . In 'Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI-91)', pp. 52-60. Morgan Kaufmann, San Mateo, CA, 1991. pdf
[CD00] J. Cheng and M. J. Druzdzel (2000) AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks , Volume 13, pages 155-188. pdf
[CFS99] T. Chen, V. Filkov and S. Skiena (1999) Identifying Gene Regulator Networks from Experimental Data. ACM-SIGAT The Third Annual International Conference on Computational Moledular Biology (RECOMB99), 1999. pdf
[CHM97] D. Chickering, D. Heckerman, and C.Meek. (1997) A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of UAI-97, pages 80-89. Morgan Kaufmann, 1997. pdf
[DM00] S. Davies and A. Moore (2000) Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous and Discrete Variables tech. report CMU-CS-00-119, Computer Science Department, Carnegie Mellon University, April, 2000. pdf
[FG96] N. Friedman and M. Goldszmidt. (1996) Context-specific independence in bayesian networks. In 'Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)', pp. 115-123. pdf
[FGKP99] L. Getoor, N. Friedman, D. Koller, and A. Pfeffer. (1999) Learning probabilistic relational models. In IJCAI '99. 1999. pdf
[FNP00] N. Friedman, I. Nachman and D. Pe'er (2000) Using Bayesian Networks to Analyze Expression Data (Submitted to RECOMB 2000). pdf
[FNP99] N. Friedman, I, Nachman, and D. Pe�er. (1999) Learning Bayesian Network Structures from Massive Datasets: The Sparse Candidate Algorithm, In 'Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)', pp. 206-215, 1999. pdf
[Fr98] N. Friedman. (1998) The Bayesian structural EM algorithm , in G. F. Cooper & S. Moral, eds., `Proc. Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI -98)', Morgan Kaufmann, San Francisco, CA. pdf
[He95] D. Heckerman. (1995)A Tutorial on Learning with Bayesian Networks , Technical Report MSR-TR-95-06, Microsoft Research, March 1995. pdf
[He97] D. Heckerman, C. Meekand G. Cooper (1997). A Bayesian approach to causal discovery . Technical Report MSR-TR97 -05. Microsoft Research. pdf
[HGC94] D. Heckerman, D. Geiger, D. Chickering (1994). Learning Bayesian networks: The Combination of Knowledge and Statistical Data .Technical Report MSR-TR-94-09, Microsoft Research, March, 1994 pdf
[Ja97] M. Jaeger (1997). Relational bayesian networks. in `Proceedings of the Thirteenth Conference on Uncertainty in Articial Intelligence (UAI-97)'. pdf
[KKR95] K. Kanazawa, D. Koller, and S.J. Russell (1995). Stochastic simulation algorithms for dynamic probabilistic networks . In Proc. UAI, 1995. pdf
[KP97] D. Koller and A. Pfeer (1997). Object-oriented Bayesian networks. In D. Geiger and P.P. Shenoy, editors, 'Proc. 13th Conf. on Uncertainty in Articial Intelligence (UAI-97)', pages 302-313, Providence, Rhode Island. pdf
[li99] Jun S. Liu. Markov Chain Monte Carlo and Related Topics Technical Report. Department of Statistics, Stanford University, 1999.
[MC97] S. Monti & G. Cooper (1998b). Learning Hybrid Bayesian Networks from Data. Learning in Graphical Models . Kluwer Academic Publishers. pdf
[MT95] P. Myllymaki and H. Tirri (1995). Constructing computationally efficient Bayesian models via unsupervised clustering . In A.Gammerman, editor, Probabilistic Reasoning and Bayesian Belief Networks, pp. 237-248. Alfred Waller Publishers, Suffolk, 1995. pdf
[Mu01a] Kevin P. Murphy (2001a)The Bayes Net Toolbox for MATLAB (2001) . pdf
[Mu01b] K. Murphy (2001b). Learning Bayes net structure from sparse data sets . Technical report, Computer Science Division, University of California, Berkeley, 2001. pdf
[Mu01c] Kevin P. Murphy (2001c).An introduction to graphical models. Technical report, Intel Research Technical Report., 2001. pdf
[Mu01d] Kevin P. Murphy (2001d). Active Learning of Causal Bayes Net Structure March 2001.
[Mu02] Kevin Murphy (2002). Learning Markov Processes To appear in The Encyclopedia of Cognitive Science L. Nadel et al. (eds), Nature Macmillan, 2002 pdf
[My94] P. Myllym�ki (1994). Using Bayesian networks for incorporating probabilistic a priori knowledge into Boltzmann machines In Proceedings of SOUTHCON-94, pp. 97-102, Orlando, FL. IEEE Press, 1994. pdf
[Pe91] J. Pearl and T. Verma (1991). A theory of inferred causation, in Principles of Knowledge Representation and Reasoning: Proceedings of the Second International Conference (Morgan Kaufmann, San Mateo, CA). pdf
[Pe98] J. Pearl (1998). Graphs, causality, and structural equation models . Sociological Methods and Research, 27, 226-84. pdf
[RMH93] A. E. Raftery, D. M. Madigan, and J. Hoeting (1993). Model selection and accounting for model uncertainty in linear regression models . TR 262, Department of Statistics, University of Washington. pdf
[RR94] Rakesh Agrawal and Ramakrishnan Srikant (1994). Fast Algorithms for Mining Association Rules . In Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, September 1994. pdf
[RS97] M. Ramoni and P. Sebastiani (1997). Discovering Bayesian networks in incomplete databases , Technical report KMI-TR-46, Knowledge Media Institute, The Open University, March 1997. pdf
[RS98] M. Ramoni, & P. Sebastiani (1998). Parameter Estimation in Bayesian Networks from Incomplete Databases . Intelligent Data Analysis, 2. 27 pdf
[SGS+01] C.Glymour, P.Spirtes, C.Meek and T.Richardson (2001).The TETRAD Project: Constraint Based Aids to Model Specification . R.Scheines, to appear in Multivariate Behavioral Research. pdf
[SGS+00] P. Spirtes, C. Glymour, R. Scheines, S. Kauffman, V. Aimale, and F. Wimberly (2000). Constructing Bayesian network models of gene expression networks from microarray data . In Proceedings of the Atlantic Symposium on Computational Biology. pdf
[SWR00] E. van Someren, L. Wessels, and M. Reinders (2000).Information extraction for modeling gene expressions , Proceedings of the 21st Symposium on Information Theory in the Benelux , pp. 215-222, 2000. pdf
[TK01] S. Tong and D. Koller (2001). Active Learning for Structure in Bayesian Networks . Seventeenth International Joint Conference on Artificial Intelligence, Seattle, Washington, August 2001, pages 863-869. pdf

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Group founded: 06 Dec 1999
Page created: 01 Jul 2001
Last updated: 24 March 2003

Roby Joehanes