List of publications

2023:

• J.H. Jui, and M. Hauskrecht.
  Uncovering the Effects of Genes, Proteins, and Medications on Functions of Wound Healing: A Dependency Rule-Based Text Mining Approach Leveraging GPT-4.
  IEEE International Conference on Biomedical and Health Informatics (BHI), October 2023.

• J. Lee, and M. Hauskrecht
  Personalized Event Prediction for Electronic Health Records
  Artificial Intelligence in Medicine journal , Elsevier (in print), 2023

• J. Wang, and M. Hauskrecht
  Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies
  International Conference on Artificial Intelligence in Medicine, June 2023

• J.H. Jui, and M. Hauskrecht
  Machine learning models for automatic Gene Ontology annotation of biological texts
  International Conference on Artificial Intelligence in Medicine, June 2023

• Z. Luo, Y. He, Y. Xue, H. Wang, M. Hauskrecht, T. Liu
  Hierarchical Active Learning With Qualitative Feedback on Regions
  IEEE Transactions on Human-Machine Systems 10.1109/THMS.2023.3252815. March 2023.

• S Malakouti, G Clermont, M Hauskrecht
  Widespread adoption of AI risk models for maternal hemorrhage requires mitigation of bias
  American Journal of Obstetrics & Gynecology 228 (1), 2023

2022:

• R. Crum, S. Johnson, P. Jiang, Peng, J Jui, R. Zamora, D. Cortes, M. Kulkarni, A. Prabahar, J. Bolin, E. Gann, E. Elster, S. Schobel, D. Larie, R. Cockrell, G An, B. Brown, M. Hauskrecht, Y. Vodovotz, S. Badylak.
  Transcriptomic, Proteomic, and Morphologic Characterization of Healing in Volumetric Muscle Loss
  Tissue Engineering, August 2022

• Jeongmin Lee, Milos Hauskrecht.
  Learning to Adapt Dynamic Clinical Event Sequences with a Residual Mixture of Experts.
  International Conference on Artificial Intelligence in Medicine, June 2022

• Salim Malakouti, Milos Hauskrecht.
  Hierarchical Deep Multi-task learning for Classification of Patient Diagnoses
  International Conference on Artificial Intelligence in Medicine, June 2022

• JH Yoon, S Malakouti, M Hauskrecht, MR Pinsky, G Clermont.
  Machine Learning Driven Prediction of Hypotension Using Real World Multi-granular Data
  ICU Management, 2022.

2021:

• Siqi Liu, Milos Hauskrecht.
  Event outlier detection in real time
  Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 6793-6803, PMLR 139, 2021.

• Jeongmin Lee, Milos Hauskrecht.
  Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
  International Conference on Artificial Intelligence in Medicine, pp. 175-186, June 2021
  best paper nominee

• Matthew Barren, Milos Hauskrecht.
  Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning
  International Conference on Artificial Intelligence in Medicine, pp. 479-490, June 2021

• Y Xue, M Hauskrecht
  A General Two-stage Multi-label Ranking Framework
  The International FLAIRS Conference Proceedings 34, 2021.

• Jeongmin Lee, Milos Hauskrecht.
  Modeling multivariate clinical event time-series with recurrent temporal mechanisms
  Artificial Intelligence in Medicine journal , volume 112, Elsevier, February 2021

2020:

• Zhipeng Luo, Milos Hauskrecht.
  Hierarchical Active Learning with Overlapping Regions
  Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM), pp. 1045-1054, October 2020.

• Jeongmin Lee, Milos Hauskrecht.
  Multi-scale Temporal Memory for Clinical Event Time-Series Prediction
  Proceedings of the International Conference on AI in Medicine (AIME), August 2020

• Salim Malakouti, Milos Hauskrecht.
  Not All Samples are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification
  Proceedings of the 33rd International FLAIRS conference, May 2020

• Jeongmin Lee, Milos Hauskrecht.
  Clinical Event Time-series Modeling with Periodic Events
  Proceedings of the 33rd International FLAIRS conference, May 2020

• Andrew J King, Gregory F Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F Sittig, Shyam Visweswaran.
  Leveraging eye tracking to prioritize relevant medical record data: Comparative machine learning study.
  Journal of Medical Internet research, vol 22., No: 4, April 2020.

• Ke Yu, Mingda Zhang, Tianyi Cui and Milos Hauskrecht.
  Monitoring Mortality Risk with Long Short-Term Memory Recurrent Neural Network.
  Pacific Symposium on Biocomputing, pp. 103-114, January 2020

2019:

• Siqi Liu and Milos Hauskrecht.
  Nonparametric Regressive Point-Processes Based on Conditional Gaussian Processes.
  Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019.
  ( Supplementary material, poster, code)

• Andrew J King, Gregory F Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F Sittig, Shyam Visweswaran.
  Using machine learning to selectively highlight patient information.
  Journal of Biomedical Informatics, vol. 100, December 2019

• Salim Malakouti and Milos Hauskrecht.
  Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification
  Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, November 2019.

• Jeongmin Lee and Milos Hauskrecht.
  Recent Context-aware LSTM for Clinical Event Time-series Prediction.
  Artificial Intelligence In Medicine (AIME), Poznan, Poland, June 2019.
  best paper award

• Salim Malakouti and Milos Hauskrecht.
  Predicting patient's diagnoses and diagnostic categories from clinical-events in EHR data.
  Artificial Intelligence In Medicine (AIME), Poznan, Poland, June 2019.

• Matteo Mantovani, Carlo Combi and Milos Hauskrecht.
  Mining compact predictive pattern sets using classification model
  Artificial Intelligence In Medicine (AIME), Poznan, Poland, June 2019 (revised version of the paper).

• Patrick Luo and Milos Hauskrecht.
  Region-Based Active Learning with Hierarchical and Adaptive Region Construction .
  SIAM Data mining conference (SDM), Calgary, Canada, May 2019.

• Yanbing Xue and Milos Hauskrecht.
  Active Learning of Multi-class Classification Models from Ordered Class Sets .
  33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, 2019.

2018:

• Andrew King, G. Cooper, H. Hocheiser, G. Clermont, M. Hauskrecht, S. Visveswaran.
  Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System
  Proceedings of AMIA Annual Symposium, 2018.

• Patrick Luo and Milos Hauskrecht.
  Hierarchical Active Learning with Proportion Feedback on Regions.
  European Conference on Machine Learning (ECML), Dublin, Ireland, September 2018.

• Siqi Liu, Adam Wright, Milos Hauskrecht.
  Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.
  Artificial Intelligence in Medicine journal, 2018.

• Patrick Luo and Milos Hauskrecht.
  Hierarchical Active Learning with Group Proportion Feedback .
  27th International Joint Conference on Artificial Intelligence (IJCAI) , Stockholm, Sweden, July 2018.

• Zitao Liu, Yan Yan and Milos Hauskrecht.
  A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic .
  41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, July 2018.

• Yanbing Xue and Milos Hauskrecht.
  Active Learning of Multi-Class Classifiers with Auxiliary Probabilistic Information.
  31th International FLAIRS Conference, Melbourne, FL, May 2018.

• Charmgil Hong and Milos Hauskrecht.
  Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data.
  31th International FLAIRS Conference, Melbourne, FL, May 2018.

2017:

• Zitao Liu, and Milos Hauskrecht.
  A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection.
  26th ACM International Conference on Information and Knowledge Management (CIKM), November 2017.

• Siqi Liu, Dean Sittig, Adam Wright, and Milos Hauskrecht.
  Change-Point Detection for Monitoring Clinical Decision Support Systems with a Multi-Process Dynamic Linear Model.
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Kansas City, MO, November 2017.

• Siqi Liu, Adam Wright, and Milos Hauskrecht.
  Change-point Detection Method for Clinical Decision Support Rule Monitoring.
  16th International Conference on Artificial Intelligence in Medicine , Vienna, Austria, June 2017.

• Zhipeng Luo, and Milos Hauskrecht.
  Group-based Active Learning of Classification Models.
  FLAIRS 30, FL, May 2017.

• Siqi Liu, Adam Wright, and Milos Hauskrecht.
  Online Conditional Outlier Detection for Nonstationary Time-series.
  FLAIRS 30, FL, May 2017.

• Yanbing Xue, and Milos Hauskrecht.
  Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking.
  FLAIRS 30, FL, May 2017 (best student paper award).

• Yanbing Xue, and Milos Hauskrecht.
  Active learning of classification models with Likert-scale feedback.
  SIAM Data Mining Conference (SDM), Houston, TX, April 2017.

2016:

• Milos Hauskrecht, I. Batal, C. Hong, Q. Nguyen, G. Cooper, S. Visweswaran, G. Clermont.
  Outlier-based detection of unusual patient-management actions: An ICU study.
  Journal of Biomedical Informatics, vol. 64, December 2016, pp 211-221.

• Yanbing Xue and Milos Hauskrecht.
  Learning of Classification Models from Noisy Soft-Labels.
  European Artificial Intelligence Conference (ECAI), Hague, Netherland, 2016.

• Zitao Liu and Milos Hauskrecht.
  Learning Linear Dynamical Systems from Multivariate Time Series: A Matrix Factorization Based Framework
  SIAM International Conference on Data Mining (SDM), Miami, FL, 2016.

• Zitao Liu and Milos Hauskrecht.
  Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data
  The 30th AAAI Conference on Artificial Intelligence(AAAI), Phoenix, AZ, 2016. (revised version with additional results)

• Charmgil Hong and Milos Hauskrecht.
  Multivariate Conditional Outlier Detection and Its Clinical Application.
  The 30th AAAI Conference on Artificial Intelligence(AAAI), Phoenix, AZ, 2016. (abstract)

• I. Batal, G. Cooper, D. Fradkin, J. Harrison, F. Moerchen, and M. Hauskrecht.
  An Efficient Pattern Mining Approach for Event Detection in Multivariate Temporal Data (review version)
  Knowledge and Information Science Journal , 46(1): 115-150, 2016 (online since 2014)

2015:

• Zitao Liu, Yan Yan, Jian Yang, and Milos Hauskrecht.
  Missing Value Estimation for Hierarchical Time Series: A Study of Hierarchical Web Traffic
  Proceedings of the IEEE International Conference on Data Mining(ICDM), Atlantic City, NJ, 2015.

• Eric Heim, and Milos Hauskrecht.
  Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels
  Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington DC, 2015.

• E. Heim, M. Berger, L.M. Seversky, and M. Hauskrecht.
  Efficient Online Relative Comparison Kernel Learning
  SIAM Data Mining Conference (SDM-15), Vancouver, Canada, April 2015.

• M. Pakdaman Naeini, G. Cooper, and M. Hauskrecht.
  Binary classifier calibration using a Bayesian non-parametric approach
  SIAM Data Mining Conference (SDM-15), Vancouver, Canada, April 2015, pp. 208-216.

• C. Hong, I. Batal, and M. Hauskrecht.
  A Generalized Mixture Framework for Multi-label Classification.
  SIAM Data Mining Conference (SDM-15), Vancouver, Canada, April 2015, pp. 712-720.

• C. Hong, and M. Hauskrecht.
  Multivariate Conditional Anomaly Detection and Its Clinical Application.
  Doctoral Consortium. The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.

• Z. Liu, and M. Hauskrecht.
  A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis.
  The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.

• M. Pakdaman Naeini, G. Cooper, and M. Hauskrecht.
  Obtaining well-calibrated probabilities using Bayesian binning.
  The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.

2014:

• Z. Liu, and M. Hauskrecht.
  Clinical time series prediction: Towards a hierarchical dynamical system framework (review version)
  Journal of Artificial Intelligence in Medicine, 65(1):5-18, September 2015 (electronic pub available on Nov 2014)

• C. Hong, I. Batal, and M. Hauskrecht.
  A Mixtures-of-Trees Framework for Multi-Label Classification
  ACM International Conference on Information and Knowledge Management (CIKM), October 2014.

• E. Heim, H. Valizadegan, and M. Hauskrecht. "
  Relative Comparison Kernel Learning with Auxiliary Kernels
  European Machine Learning Conference (ECML), September 2014.

• M. Pakdaman Naeini, I. Batal, Z. Liu, C. Hong, and M. Hauskrecht.
  An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification
  SIAM Data Mining Conference, April 2014.

• Q. Nguyen, H. Valizadegan, and M. Hauskrecht.
  Learning classification models with soft-label information.
  Journal of American Medical Informatics Association, 21:3, pp. 501-508, 2014. DOI information: 10.1016/j.jbi.2013.08.007 doi:10.1136/amiajnl-2013-001964, 2013.

2013:

• Z. Liu, and M. Hauskrecht.
  Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series.
  NIPS 2013 Workshop on Machine Learning for Clinical Data Analysis and Healthcare, December 2013.

• I. Batal, C. Hong, and M. Hauskrecht.
  An Efficient Probabilistic Framework for Multi-Dimensional Classification.
  Proceedings of the 19th ACM international conference on Information and knowledge management , November 2013.

• H. Valizadegan, Q. Nguyen, and M. Hauskrecht.
  Learning Classification Models from Multiple Experts.
  Journal of Biomedical Informatics, 46:6, pp. 1125-1135, 2013. DOI information: http://dx.doi.org/10.1016/j.jbi.2013.08.007, 2013.

• Hauskrecht, S. Visweswaran, G. Cooper and G. Clermont.
  Data-driven identification of unusual clinical actions in the ICU.
  Annual American Medical Informatics Association Symposium, Washington, DC, 2013.

• A. Amizadeh, B. Thiesson, M. Hauskrecht.
  The Bregman Variational Dual-Tree Framework.
  The 29th International Conference on Uncertainty in Artificial Intelligence (UAI), Seattle, WA, July 2013.

• Hauskrecht, S. Visweswaran, G. Cooper and G. Clermont.
  Conditional outlier approach for detection of unusual patient care actions.
  The Twenty-Seventh AAAI Conference on Artificial Intelligence, Seattle, WA, July 2013.

• Z. Liu, and M. Hauskrecht.
  Clinical Time Series Prediction with a Hierarchical Dynamical System.
  The 14th Conference on Artificial Intelligence in Medicine, Murcia, Spain, May 2013.

• I. Batal, H. Valizadegan, G. Cooper and M. Hauskrecht.
  A Temporal Pattern Mining Approach for Classifying Electronic Health Record Data.
  Transactions on Intelligent Systems and Technology, Special Issue on Health Informatics, 4: 4, 2013. ( submitted version )

• Z. Liu, L. Wu, and M. Hauskrecht.
  Modeling Clinical Time Series Using Gaussian Process Sequences.
  SIAM Data Mining Conference , Austin, TX, April 2013.

2012:

• M. Hauskrecht, I. Batal, M. Valko, S. Visweswaran, G. Cooper, G. Clermont.
  Outlier-detection for patient monitoring and alerting.
  Journal of Biomedical Informatics, 46:1, pages 47 -- 55, February 2013.

• H. Valizadegan, Q. Nguyen, and M. Hauskrecht.
  Learning Medical Diagnosis Models from Multiple Experts.
  Annual American Medical Informatics Association Symposium , Chicago, IL, November 2012.

• S. Wang, M. Hauskrecht.
  Keyword Annotation of Biomedical Documents with Graph-based Similarity Methods.
  IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Philadelphia, October 2012.

• I. Batal, G. Cooper, and M. Hauskrecht.
  A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.
  The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK, September 2012.

• S. Amizadeh, B. Thiesson, and M. Hauskrecht.
  Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation.
  The 28th International Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, CA, August 2012.

• I. Batal, D. Fradkin, J. Harrison, F. Moerchen, and M. Hauskrecht.
  Mining Recent Temporal Patterns for Event Detection in Multivariate Time Series Data.
  The 18th ACMSIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Beijing, China, August 2012.

• S. Amizadeh, H. Valizadegan, and M. Hauskrecht.
  Factorized Diffusion Map Approximation.
  15th International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, April 2012. ( appendix)

• Z. Liu, L. Wu, and M. Hauskrecht.
  State-space Gaussian Process Prediction.
  ICML workshop on Clinical Data Analysis, Edinburgh, Scotland, June 2012.

• H. Valizadegan, S. Amizadeh, M. Hauskrecht.
  Sampling Strategies to Evaluate the Performance of Unknown Predictors.
  SIAM Data Mining Conference, Anaheim, CA, April 2012.

• Yuriy Sverchkov, S.Visweswaran, G. Clermont, M. Hauskrecht, and G. Cooper.
  A Multivariate Probabilistic Method for Comparing Two Clinical Datasets.
  ACM International Health Informatics Symposium, January 2012.

2011:

• Q. Nguyen, H. Valizadegan, and M. Hauskrecht.
  Learning classification with auxiliary probabilistic information.
  IEEE International Conference on Data Mining, Vancouver, Canada, pp. 477-486, December 2011.

• M. Valko, H. Valizadegan, B. Kveton, GF. Cooper, and M. Hauskrecht.
  Conditional Anomaly Detection with Soft Harmonic Functions.
  IEEE International Conference on Data Mining, Vancouver, Canada, pp. 735-743, December 2011.

• TC. Hart, PM. Corby, M. Hauskrecht, OH Ryu, R. Pelikan, M. Valko, MB. Oliveira, GT. Hoehn, and WA. Bretz.
  Identification of Microbial and Proteomic Biomarkers in Early Childhood Caries.
  International Journal of Dentistry, 2011.

• I. Batal, H. Valizadegan, GF. Cooper, and M. Hauskrecht.
  A Pattern Mining Approach for Classifying Multivariate Temporal Data.
  IEEE International Conference on Bioinformatics and Biomedicine , Atlanta, Georgia, November 2011.

• Q. Nguyen, H. Valizadegan, A. Seybert, and M. Hauskrecht.
  Sample-efficient learning with auxiliary class-label information.
  Annual American Medical Informatics Association (AMIA) Symposium , October 2011.

• S. Amizadeh, S. Wang, and M. Hauskrecht.
  An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics.
  International Joint Conference on AI (IJCAI), Barcelona, Spain, July 2011.

• M. Valko, H. Valizadegan, B. Kveton, GF. Cooper, and M. Hauskrecht.
  Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting.
  ICML Workshop For Global Challenges, Bellevue, Washington, June 2011.

2010:

• M. Hauskrecht, M. Valko, I.Batal, G. Clermont, S. Visweswaran, G. Cooper.
  Conditional Outlier Detection for Clinical Alerting.
  Annual American Medical Informatics Association (AMIA) Symposium , November 2010 [Homer Warner Award]

• I. Batal, and M. Hauskrecht.
  Mining Clinical Data using Minimal Predictive Rules.
  Annual American Medical Informatics Association (AMIA) Symposium , November 2010.

• S. Visweswaran, J. Mezger, G. Clermont, M. Hauskrecht, G. Cooper.
  Identifying Deviations from Usual Medical Care using a Statistical Approach.
  Annual American Medical Informatics Association (AMIA) Symposium , November 2010.

• R. Pelikan and M. Hauskrecht.
  Automatic Selection of Preprocessing Methods for Mass Spectrometry Data.
  Annual American Medical Informatics Association (AMIA) Symposium , November 2010.

• I. Batal, M. Hauskrecht.
  Constructing Classification Features using Minimal Predictive Patterns.
  Proceedings of the 19th ACM international conference on Information and knowledge management , November 2010, pp. 869-878.

• I. Batal, M. Hauskrecht.
  A Concise Representation of Association Rules using Minimal Predictive Rules.
  European Conference on Machine Learning and Knowledge Discovery in Databases, September 2010.

• S. Amizadeh, and M. Hauskrecht.
  Latent Variable Model for Learning in Pairwise Markov Networks.
  Proceeding of the National Conference on Artificial Intelligence (AAAI) , Atlanta, GA, July 2010.

• S. Wang, and M. Hauskrecht.
  Effective Query Expansion with the Resistance Based Term Similarity Metric.
  Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval , July 2010, pp. 715-716.

• M. Valko, and M. Hauskrecht.
  Feature importance analysis for patient management decisions.
  13th International Congress on Medical Informatics , Cape Town, South Africa, September 2010.

• S. Wang, M. Hauskrecht, S. Visweswaran.
  Candidate Gene Prioritization Using Network Based Probabilistic Models.
  AMIA Summit on Translation Bioinformatics , March 2010.

2009:

• T. Singliar, and M. Hauskrecht.
  Learning to detect incidents from noisily labeled data.
  Machine Learning Journal, September 2009.

• I. Batal, L. Sacchi, R. Bellazzi, and M. Hauskrecht.
  A Temporal Abstraction Framework for Classifying Clinical Temporal Data .
  Annual American Medical Informatics Association (AMIA) Symposium , 2009.

• I. Batal, M. Hauskrecht.
  Boosting KNN Text Classification Accuracy by using Supervised Term Weighting Schemes.
  18th ACM Conference on Information and Knowledge Management (CIKM) , 2009.

• S. Wang, S. Visweswaran, and M. Hauskrecht.
  Learning Probabilistic Knowledge Model for Document Retrieval.
  International Conference on Knowledge Discovery and Information Retrieval , 2009.

• S. Wang, M. Hauskrecht, S. Visweswaran.
  Gene prioritization using a probabilistic knowledge model.
  IEEE International Conference on Bioinformatics and Biomedicine, Workshop on Graph Techniques for Biomedical Networks, pp. 272 - 278, 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.
  Proceedings of the Twenty-Second International Florida AI Research Society Conference (FLAIRS 2009), May 2009.

2008:

• Branislav Kveton, Milos Hauskrecht.
  Partitioned Linear Programming Approximations for MDPs.
  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.
  Proceedings of the 31st Annual International ACM SIGIR Conference , Singapore, July 2008.

• M. Valko and M. Hauskrecht.
  Distance metric learning for conditional anomaly detection.
  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.
  AMIA Summit on Translational Bioinformatics, San Francisco, CA, March 2008.

• M. Hauskrecht, R. Pelikan.
  Inter-session reproducibility measures for high-throughput data sources.
  AMIA 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.

2007

• R. Pelikan, W.Bigbee, D. Malehorn, J. Lyons-Weiler, 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.
  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 European 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.

2006

• W. W. Chapman, J. N. Dowling, G. F. Cooper, M. Hauskrecht, M. Valko.
  Comparison of Chief Complaints and Emergency Department Reports for Identifying Patients with Acute Lower Respiratory Syndrome.
  Proceedings of the National Syndromic Surveillance Conference, 2006.

• 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.
  Fundamentals of Data Mining in Genomics and Proteomics, eds. Berrar, Dubitzky, Granzow. pp. 149-172, 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, pp. 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.

2005

• 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.
  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.

2004

• 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.
  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.
  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.
  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.

2003

• M. Hauskrecht, B. Kveton.
  Linear program approximations for factored continuous-state Markov Decision Processes.
  Advances in Neural Information Processing Systems 16 , pages 895- 902, 2003.

• 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.

2001

• Milos Hauskrecht, Nicolas Meuleau, Leslie Kaelbling, Thomas Dean, Craig Boutiler
  Decision theoretic planning with temporally abstract actions .
  unpublished manuscript, 2001.

• Milos Hauskrecht, Eli 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.

• Milos 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.

• Milos 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.

2000

• Milos Hauskrecht.
  Value-function approximations for partially observable Markov decision processes .
  Journal of Artificial Intelligence Research, vol.13, pp. 33-94, 2000.

• Milos 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.

• Milos 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.

1999

• Milos Hauskrecht, Gopal Pandurangan, Eli 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.

1998

• Milos Hauskrecht, Hamish 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.

• Milos Hauskrecht, Hamish 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.

1997

• Milos Hauskrecht.
  Planning and control in stochastic domains with imperfect information.
  PhD dissertation , MIT-LCS-TR-738, 1997.

• Milos 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.

• Milos 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.

1996 and before

• 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.

PhD dissertation

• M. Hauskrecht.
  Planning and control in stochastic domains with imperfect information.
  PhD dissertation , MIT-LCS-TR-738, 1997.

MS thesis

• M. Hauskrecht.
  A planning mechanism for selecting inference goals in a diagnostic expert system.
  MS thesis , EF STU, Bratislava, May 1988.

Technical Reports and other papers

• I. Batal, and M. Hauskrecht. Mining predictive patterns in Electronic Health Record data , CS Technical report, 2013.

• I. Batal. Mining Predictive Temporal Patterns and its Extension to Multivariate Temporal Data, PhD Dissertation, CS department, October 2012.

• M. Hauskrecht. Overview of Linear Program Approximations for Factored Continuous and Hybrid-State Markov Decision Processes. CS Technical report, 2012.

• 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.

Theses of my PhD students

• Branislav Kveton. Planning in hybrid structured domains, PhD thesis, University of Pittsburgh, Fall 2006.

• Tomas Singliar. Machine Learning Solutions for Transportation Networks, PhD thesis, University of Pittsburgh, Fall 2008.

• Richard Pelikan. Analytical Techniques for the Improvement of Mass Spectrometry Protein Profiling, PhD thesis, University of Pittsburgh, Spring 2011.

• Michal Valko. Adaptive Graph-based Methods for Conditional Anomaly Detection and Semi-Supervised Learning, PhD thesis, University of Pittsburgh, Summer 2011.

• Iyad Batal. Mining Predictive Temporal Patterns and Extension to Multivariate temporal, PhD thesis, University of Pittsburgh, Fall 2012.

• Saeed Amizadeh. Non-parametric graph-based methods for large scale problems. PhD thesis, University of Pittsburgh, Fall 2013.

• Quang Nguyen. Efficient learning with soft label information and multiple annotators. PhD thesis, University of Pittsburgh, Spring 2014.