- A Clustering Approach to Solving Large Stochastic Matching Problems Milos Hauskrecht
- Learning to Detect Adverse Traffic Events from Noisily Labeled Data
- Journal of Artificial Intelligence Research 27 (2006) 153201 Submitted 05/06; published 10/06 Solving Factored MDPs with Hybrid State and Action
- Journal of Machine Learning Research 7 (2006) 21892213 Submitted 01/2006; Published 10/2006 Noisy-OR Component Analysis
- Section VII New technologies
- A Concise Representation of Association Rules using Minimal Predictive Rules
- Latent Variable Model for Learning in Pairwise Markov Networks Saeed Amizadeh1
- Intersession Reproducibility of Mass Spectrometry Profiles and its Effect on Accuracy of Multivariate
- Evidence-based Anomaly Detection in Clinical Domains Milos Hauskrecht, PhD1-3
- Enhancing the analysis of mass-spectrometry proteomic profiles using prior knowledge and past data repositories
- Modeling Highway Traffic Volumes Tomas Singliar1
- Learning Basis Functions in Hybrid Domains Branislav Kveton
- Monte-Carlo optimizations for resource allocation problems in stochastic network systems
- A Clustering Approach to Solving Large Stochastic Matching Problems Milos Hauskrecht
- Applied Artificial Intelligence, vol 15 (2001), pages 429-452 Submitted 7/00; Accepted: 11/00 Efficient methods for computing investment strategies for
- Incremental methods for computing bounds in partially observable Markov decision processes
- Technical Report: CS-03-02 Monte-Carlo Approximations to Continuous-time
- redundant linear Value-function
- Towards a Learning Traffic Incident Detection System Tomas Singliar and Milos Hauskrecht
- Approximate Linear Programming for Solving Hybrid Factored MDPs
- Peptide Identification in Whole-Sample Mass Spectrometry Proteomics Richard Pelikan1,3
- A Comparison of Chief Complaints and Emergency Department Reports for Identifying Patients with Acute Lower Respiratory Syndrome
- Solving Factored MDPs with Continuous and Discrete Variables Carlos Guestrin
- Computing Near Optimal Strategies for Stochastic Investment Planning Problems Milos Hauskrecht1
- Appl Bioinformatics 2005; 4 (4): 227-246 ORIGINAL RESEARCH ARTICLE 1175-5636/05/0004-0227/$34.95/0
- MODELING CELLULAR PROCESSES WITH VARIATIONAL BAYESIAN COOPERATIVE VECTOR QUANTIZER
- Combining perfectly and partially observable MDPs Milos Hauskrecht
- Modeling Treatment of Ischemic Heart Disease with Partially Observable Markov Decision Processes.
- Hierarchical Solution of Markov Decision Processes using Macroactions Milos Hauskrecht, Nicolas Meuleau
- Planning Medical Therapy Using Partially Observable Markov Decision Milos Hauskrecht
- Computing Near Optimal Strategies for Stochastic Investment Planning Problems Milos Hauskrecht 1 , Gopal Pandurangan 1;2 and Eli Upfal 1;2
- Variational Bayesian Learning of Cooperative Vector Quantizer Model -The Theory
- Distance Metric Learning for Conditional Anomaly Detection Michal Valko and Milos Hauskrecht
- Incremental methods for computing bounds in partially observable Markov decision processes
- Solving Very Large Weakly Coupled Markov Decision Processes Nicolas Meuleau, Milos Hauskrecht,
- Journal of Artificial Intelligence Research 13 (2000) 3394 Submitted 9/99; published 8/00 Value-Function Approximations for Partially Observable
- Applied Arti cial Intelligence, in press (2000), 19 pages Submitted 7/00; Accepted: 11/00 Ecient methods for computing investment strategies for
- Linear Program Approximations for Factored Continuous-State Markov Decision Processes
- Artificial Intelligence in Medicine, vol 18 (2000), pp. 221244 Submitted 5/99; Accepted 09/99 Planning Treatment of Ischemic Heart Disease with Partially
- Appeared in the Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Canada, July 2004. Solving Factored MDPs with Continuous and Discrete Variables
- Dynamic Decision Making in Stochastic Partially Observable Medical Domains: Ischemic Heart Disease
- Modeling Treatment of Ischemic Heart Disease with Partially Observable Markov Decision Processes.
- Technical Report: CS-03-01 Monte-Carlo optimizations for resource
- Variational Learning for Noisy-OR Component Analysis Tomas Singliar and Milos Hauskrecht
- Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes
- Artificial Intelligence in Medicine, vol 18 (2000), pp. 221--244 Submitted 5/99; Accepted 09/99 Planning Treatment of Ischemic Heart Disease with Partially
- Detecting Deviations from Usual Medical Care James Mezger1
- Learning predictive models for combinations of heterogeneous proteomic data sources
- Conditional anomaly detection methods for patientmanagement alert systems
- Solving Factored MDPs with Exponential-Family Transition Models Branislav Kveton
- Feature Selection and Dimensionality Reduction in Genomics and Proteomics
- Planning with macro-actions: E ect of initial value function estimate on convergence rate of
- An MCMC Approach to Solving Hybrid Factored MDPs Branislav Kveton
- A Pattern Mining Approach for Classifying Multivariate Temporal Data Iyad Batal, Hamed Valizadegan, Gregory F. Cooper and Milos Hauskrecht
- Conditional Outlier Detection for Clinical Alerting Milos Hauskrecht, PhD1
- Conditional Anomaly Detection with Soft Harmonic Functions Michal Valko0
- An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics
- Partitioned Linear Programming Approximations for MDPs Branislav Kveton
- Mining Clinical Data using Minimal Predictive Rules Iyad Batal1
- Learning classification with auxiliary probabilistic information Quang Nguyen, Hamed Valizadegan, Milos Hauskrecht
