T. Singliar, and M. Hauskrecht. Learning to detect incidents from noisily labeled data. Machine Learning Journal, to appear
I. Batal, Lucia Sacchi, Riccardo Bellazzi, and Milos Hauskrecht. A Temporal Abstraction Framework for Classifying Clinical Temporal Data. AMIA anual symposium , 2009 (to appear)
I. Batal, M. Hauskrecht. Boosting KNN Text Classification Accuracy by using Supervised Term Weighting Schemes 18th ACM Conference on Information and Knowledge Management , 2009 (to appear)
S. Wang, S. Visweswaran, and M. Hauskrecht. Learning Probabilistic Knowledge Model for Document Retrieval. International Conference on Knowledge Discovery and Information Retrieval , 2009.
I. Batal, L. Sacchi, R. Bellazzi, and M. Hauskrecht. Multivariate Time Series Classification with Temporal Abstractions. In Proceedings of the Twenty-Second International Florida AI Research Society Conference (FLAIRS 2009), May 2009.
S. Wang and M. Hauskrecht, Improving Biomedical Document Retrieval by Mining Domain Knowledge. In Proceedings of the Twenty-Second International Florida AI Research Society Conference (FLAIRS 2009), May 2009.
Branislav Kveton, Milos Hauskrecht. Partitioned Linear Programming Approximations for MDPs. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, Helsinki, Finland, July 2008.
Michal Valko, Gregory Cooper, Amy Seybert, Shyam Visweswaran, Melissa Saul, Milos Hauskrecht. Conditional anomaly detection methods for patient-management alert systems. Proceedings of the ICML Workshop on Machine Learning in Health Care Applications, 25th International Machine Learning Conference, Helsinki, Finland, 2008.
S. Wang, M. Hauskrecht. Improving Biomedical Document Retrieval using Domain Knowledge. In Proceedings of the 31st Annual International ACM SIGIR Conference , Singapore, July 2008.
M. Valko and M. Hauskrecht. Distance metric learning for conditional anomaly detection. In Proceedings of the Twenty-First International Florida AI Research Society Conference (FLAIRS 2008), May 2008.
R. Pelikan and M. Hauskrecht. Efficient peak labeling algorithms for whole-sample mass spectrometry proteomics. IEEE Transactions on Computational Biology and Bioinformatics, 2008.
M. Valko, R. Pelikan and M. Hauskrecht. Learning predictive models for multiple heterogeneous proteomic data sources. In Proceedings of the Summit on Translational Bioinformatics, San Francisco, CA, March 2008.
M. Hauskrecht, R. Pelikan. Inter-session reproducibility measures for high-throughput data sources. In Proceedings of the Summit on Translational Bioinformatics, San Francisco, CA, March 2008.
T. Singliar, M. Hauskrecht. Approximation strategies for routing in stochastic dynamic networks. In Proceedings of the Tenth International Symposium on Artificial Intelligence and Mathematics , Ft. Lauderdale, FL, January 2008.
R. Pelikan, W.Bigbee, D. Malehorn, J. Lyons-Weiler, and M. Hauskrecht Intersession Reproducibility of Mass Spectrometry Proteomic Profiles and its Effect on the Accuracy of Multivariate Classification Models. Bioinformatics , 23 (22), pp. 3065-3072, 2007.
M. Hauskrecht, M. Valko, B. Kveton, S. Visweswaram, G. Cooper. Evidence-based anomaly detection in clinical domains. In Proceedings of the Annual American Medical Informatics Association (AMIA) conference,, 2007.
R. Pelikan, M. Hauskrecht. Peptide Identification in Whole-Sample Mass Spectrometry Proteomics. Annual American Medical Infortmatics Association (AMIA) Conference, poster abstract, 2007
J. Mezger, G. F. Cooper, M. Hauskrecht, G. Clermont, S Visweswaran. Detecting Deviations from Usual Medical Care. Annual American Medical Infortmatics Association (AMIA) Conference, poster abstract, 2007.
M. Hauskrecht, R. Pelikan. Enhancing the analysis of MS proteomic profiles using prior knowledge and past data repositories. Proceedings of 39th Symposium on the Interface of Computing Science and Statistics: Systems Biology , 2007.
T. Singliar and M. Hauskrecht. Modeling Highway Traffic Volumes. Proceedings of Eighteen Europian Conference on Machine Learning (ECML), 2007.
T. Singliar and M. Hauskrecht. Learning to detect traffic incidents from imperfectly labeled data. Proceedings of Eleventh International Conference on Principles of Knowledge Discovery in Databases, , pp. 236-247 2007.
T. Singliar and M. Hauskrecht. Noisy-or Component Analysis and its Application to Link Analysis. Journal of Machine Learning Research , vol 7, pp. 2189-2213, 2006
B. Kveton, M. Hauskrecht, C. Guestrin. Solving Factored MDPs with Hybrid State and Action Variables. Journal of Artificial Intelligence Research , vol 27, pp. 153-201, 2006.
T. Jahnukainen, D. Malehorn, M. Sun, J. Lyons-Weiler,W. Bigbee, G. Gupta, R.Shapiro, P. Randhawa, R. Pelikan, M. Hauskrecht, A. Vats. Proteomic Analysis of Urine in Kidney Transplant Patients with BK Virus Nephropathy. Journal of American Society of Nephrology (JASN) , vol 17, pp. 3248-3256, 2006.
M. Hauskrecht, R. Pelikan, M. Valko, J. Lyons-Weiler. Feature Selection and Dimensionality Reduction in Genomics and Proteomics. In Fundamentals of Data Mining in Genomics and Proteomics, eds. Berrar, Dubitzky, Granzow. Springer, Fall 2006.
B. Kveton and M. Hauskrecht. Learning Basis Functions in Hybrid Domains. Proceedings of the 21st National Conference on AI (AAAI-06), Boston, MA, pages 1161-1166, July 2006.
T. Singliar and M. Hauskrecht. Towards a learning traffic incident detection system. ICML 2006 Workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, June 2006.
B. Kveton and M. Hauskrecht. Solving Factored MDPs with Exponential-Family Transition Models. Proceedings of the 16th International Conference on Planning and Scheduling , UK, pages 114-120, June 2006.
D. Mosse, L. Comfort, A. Labrinidis, A. Amer, J. Brustoloni, P. Chrysanthis, M. Hauskrecht, T. Znati, R. Melhem, K. Pruhs. Secure-CITI Project Highlights. Featured in the 7th Annual International Conference on Digital Government Research (dg.o 2006) San Diego, CA, May 2006.
M. Hauskrecht, B. Kveton. Approximate Linear Programming for Solving Hybrid Factored MDPs. Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, Fort Lauderdale, Florida, January 2006.
B. Kveton and M. Hauskrecht. An MCMC Approach to Solving Hybrid Factored MDPs. In Proceedings of the 19th International Joint Conference on Artificial Intelligence , Edinburgh, Scotland, August 2005.
T. Singliar and M. Hauskrecht. Variational Learning for the Noisy-OR Component Analysis. In the Proceedings of the SIAM International Data Mining conference, pp. 370--379 , 2005.
M. Hauskrecht, R. Pelikan, W.L. Bigbee, D. Malehorn, M.T. Lotze, H.J. Zeh, D.C. Whitcomb, and J. Lyons-Weiler. Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles, Applied Bioinformatics , 4:4, pp. 227-246, 2005.
J. Lyons-Weiler, R. Pelikan, H.J. Zeh III, D.C. Whitcomb, D.E. Malehorn, W.L. Bigbee and M. Hauskrecht. Assessing the Statistical Significance of the Achieved Classification Error of Classifiers Constructed Using Serum Peptide Profiles and a Prescription for Random Resampling Repeated Studies for Massive High-Throughput Genomic and Proteomic Studies , Cancer Informatics,1:1, pp. 53-77, 2005.
R. Pelikan, M. Lotze, J. Lyons-Weiler, D. Malehorn and M. Hauskrecht. Serum Proteomic Profiling and Analysis. In Lotze MT, Thomson AW, eds. Measuring Immunity: Basic Biology and Clinical Applications, Elsevier, London, 2004.
L.K. Comfort, M. Hauskrecht, J.S. Lin. Dynamic Networks: Modeling Change in Environments Exposed to Risk. Annual Research Conference of the Association of Public Policy and Management, Atlanta, Georgia, October 28-30, 2004.
C. Guestrin, M. Hauskrecht, B. Kveton. Solving Factored MDPs with Continuous and Discrete Variables. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pages 235-242, 2004.
C. Guestrin, M. Hauskrecht, B. Kveton. Solving Factored MDPs with Continuous and Discrete Variables. In Proceedings of AAAI Workshop on Learning and Planning in Markov Processes - Advances and Challenges , pages 19-24, 2004.
V. Grigorian, D, Chiarulli, M. Hauskrecht. Subject Filtering for Passive Biometric Monitoring. In Proceedings of the 2004 Meeting of the International Federation of Classification Societies (IFCS04), pages 485-494, 2004.
B. Kveton, M. Hauskrecht. Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes. In Proceedings of the 14th International Conference on Automated Planning and Scheduling, pages 306-314, 2004.
X. Lu, M. Hauskrecht, R.S. Day. Modeling cellular processes with variational Bayesian cooperative vector quantizer. In the Proceedings of the Pacific Symposium on Biocomputing (PSB), pp.533-544, 2004.
M. Hauskrecht, B. Kveton. Linear program approximations for factored continuous-state Markov Decision Processes. In Advances in Neural Information Processing Systems 16 , pages 895- 902, 2004.
M. Hauskrecht, T. Singliar. Monte Carlo optimizations for resource allocation problems in stochastic network systems. In Proceedings of the Nineteenth International Conference on Uncertainty in Artificial Intelligence, pp. 305-312, 2003.
M. Hauskrecht, E. Upfal. A clustering approach to solving large stochastic matching problems. In Proceedings of the Seventeenth International Conference on Uncertainty in Artificial Intelligence, pp. 219-226, 2001.
M. Hauskrecht. Evaluation and optimization of management plans in stochastic domains with imperfect information. In Proceedings of the Twelfth International Workshop on Principles of Diagnosis, pp. 71--78, 2001.
M. Hauskrecht, L. Ortiz, I. Tsochantaridis, E. Upfal. Efficient methods for computing investment strategies for multi-market commodity trading. Applied Artificial Intelligence, vol. 15, pp. 429-452, 2001.
M. Hauskrecht. Value-function approximations for partially observable Markov decision processes . Journal of Artificial Intelligence Research, vol.13, pp. 33-94, 2000.
M. Hauskrecht, H. Fraser. Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artificial Intelligence in Medicine, vol. 18, pp. 221-244, 2000.
M. Hauskrecht, L. Ortiz, I. Tsochantaridis, E. Upfal. Computing global strategies for multi-market commodity trading. In Proceedings of the Fifth International Conference on Artificial Intelligence Planning and Scheduling, pp. 159--166, 2000.
M. Hauskrecht, G. Pandurangan, E. Upfal. Computing near optimal strategies for stochastic investment planning problems. In Proceedings of the 16-th International Joint Conference on Artificial Intelligence, pp. 1310-1315, 1999.
M. Hauskrecht, H. Fraser. Modeling Treatment of Ischemic Heart Disease with Partially Observable Markov Decision Processes. In Proceedings of American Medical Informatics Association annual symposium on Computer Applications in Health Care , Orlando, Florida, pp. 538-542, 1998.
M. Hauskrecht, N. Meuleau, C. Boutilier, L. Pack Kaelbling, T. Dean. Hierarchical solution of Markov decision processes using macro-actions. In Proceedings of the 14-th Conference on Uncertainty in Artificial Intelligence, pp. 220-229, 1998.
M. Hauskrecht, H. Fraser. Planning medical therapy using partially observable Markov decision processes. In Proceedings of the 9-th International Workshop on Principles of Diagnosis (DX-98), Cape Cod, MA, pp. 182-189, 1998.
N. Meuleau, M. Hauskrecht, K. Kim, L. Peshkin, L. Pack Kaelbling, T. Dean, C. Boutilier. Solving very large weakly-coupled Markov decision processes. In Proceedings of the 15-th National Conference on Artificial Intelligence, Madison, WI, pp. 165-172, 1998.
M. Hauskrecht. Planning and control in stochastic domains with imperfect information. PhD dissertation, MIT-LCS-TR-738, 1997.
M. Hauskrecht. Incremental methods for computing bounds in partially observable Markov decision processes. In Proceedings of the 14-th National Conference on Artificial Intelligence, Providence, RI, pp. 734-739, 1997.
M. Hauskrecht. Dynamic decision making in stochastic partially observable medical domains: ischemic heart disease example. In Proceedings of AI in Medicine Europe (AIME), Grenoble, France, pp. 296-299, 1997.
M. Hauskrecht. Dynamic decision making in stochastic partially observable medical domains. In Proceedings of AAAI symposium on AI in Medicine, Stanford University, pp. 69-72, 1996.
M. Hauskrecht. Tradeoffs in approaches to the ventilator controller design. In Proceedings of AAAI Symposium on AI in Medicine, Stanford University, pp. 72-75, 1994.
M. Popper, M. Hauskrecht. Declarative and operational in knowledge based systems. In Proceedings of Medical Informatics Europe, pp. 299-303, 1991.
J. Stanek, M. Popper, M. Hauskrecht. The operational aspects of an object oriented approach in a medical expert system design. In Proceedings of Medical Informatics Europe, pp. 304-308, 1991.
M. Popper, M. Hauskrecht, J. Stanek. The role of operational knowledge in knowledge based systems design. In Proceedings of Applications of Artificial Intelligence, Prague, Czechoslovakia, pp. 53-60, 1991.
M. Hauskrecht. Planning and control in stochastic domains with imperfect information. PhD dissertation, MIT-LCS-TR-738, 1997.
M. Hauskrecht. A planning mechanism for selecting inference goals in a diagnostic expert system. MS thesis, EF STU, Bratislava, May 1988.
M. Hauskrecht and T. Singliar. Monte-Carlo approximations to resource allocation problems in stochastic networks. Technical Report, CS-03-01, University of Pittsburgh, 2003.
M. Hauskrecht. Monte-Carlo approximations to continuous-time semi-Markov processes. Technical Report, CS-03-02, University of Pittsburgh , 2003.
X. Lu, M. Hauskrecht, R.S. Day. Variational Bayesian learning of the cooperative vector quantizer model. Part I: The Theory. Technical Report, Center for Biomedical Informatics, CBMI-02-181, 2002.
M. Hauskrecht. Planning with temporally abstract actions. TR-CS-98-01, Brown University, Providence, RI, 1998.
M. Hauskrecht. Planning with macro-actions: Effect of initial value function estimate on convergence rate of value iteration. Working paper, 1998.
M. Hauskrecht. Combining fully and partially observable MDPs. Working paper, 1998.
M. Hauskrecht. Learning Bayesian belief networks from data. MIT EECS Area exam, 1995.
M. Hauskrecht. Reinforcement learning of control policies. Working paper, 1994.
R. Bodkin, M. Hauskrecht. Examining recursive decomposition. Working paper, 1992