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2020 – today
- 2024
- [c110]Xiangru Chen
, Milos Hauskrecht
Enhancing Hypotension Prediction in Real-Time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms. AIME (1) 2024: 46-51 - 2023
- [j20]Jeongmin Lee
Personalized event prediction for Electronic Health Records. Artif. Intell. Medicine 143: 102620 (2023) - [j19]Zhipeng Luo
Hierarchical Active Learning With Qualitative Feedback on Regions. IEEE Trans. Hum. Mach. Syst. 53(3): 581-589 (2023) - [c109]Jayati H. Jui
Machine Learning Models for Automatic Gene Ontology Annotation of Biological Texts. AIME 2023: 199-204 - [c108]Junheng Wang
Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies. AIME 2023: 260-270 - [c107]Jayati H. Jui, Milos Hauskrecht:
Uncovering the Effects of Genes, Proteins, and Medications on Functions of Wound Healing: A Dependency Rule-Based Text Mining Approach Leveraging GPT-4 based Evaluation. BHI 2023: 1-4 - [i25]Jeongmin Lee, Milos Hauskrecht:
Personalized Event Prediction for Electronic Health Records. CoRR abs/2308.11013 (2023) - 2022
- [c106]Salim Malakouti
Hierarchical Deep Multi-task Learning for Classification of Patient Diagnoses. AIME 2022: 122-132 - [c105]Jeongmin Lee
Learning to Adapt Dynamic Clinical Event Sequences with Residual Mixture of Experts. AIME 2022: 155-166 - [i24]Jeongmin Lee, Milos Hauskrecht:
Learning to Adapt Clinical Sequences with Residual Mixture of Experts. CoRR abs/2204.02687 (2022) - 2021
- [j18]Jeongmin Lee, Milos Hauskrecht:
Modeling multivariate clinical event time-series with recurrent temporal mechanisms. Artif. Intell. Medicine 112: 102021 (2021) - [c104]Jeongmin Lee
Neural Clinical Event Sequence Prediction Through Personalized Online Adaptive Learning. AIME 2021: 175-186 - [c103]Matthew Barren
Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning. AIME 2021: 479-490 - [c102]Yanbing Xue, Milos Hauskrecht:
A General Two-stage Multi-label Ranking Framework. FLAIRS 2021 - [c101]Siqi Liu, Milos Hauskrecht:
Event Outlier Detection in Continuous Time. ICML 2021: 6793-6803 - [i23]Jeongmin Lee, Milos Hauskrecht:
Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning. CoRR abs/2104.01787 (2021) - [i22]Matthew Barren, Milos Hauskrecht:
Improving Prediction of Low-Prior Clinical Events with Simultaneous General Patient-State Representation Learning. CoRR abs/2106.14838 (2021) - 2020
- [c100]Jeongmin Lee, Milos Hauskrecht:
Multi-scale Temporal Memory for Clinical Event Time-Series Prediction. AIME 2020: 313-324 - [c99]Zhipeng Luo, Milos Hauskrecht:
Hierarchical Active Learning with Overlapping Regions. CIKM 2020: 1045-1054 - [c98]Jeongmin Lee, Milos Hauskrecht:
Clinical Event Time-Series Modeling with Periodic Events. FLAIRS 2020: 94-99 - [c97]Salim Malakouti, Milos Hauskrecht:
Not All Samples are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification. FLAIRS 2020: 323-328 - [c96]Ke Yu, Mingda Zhang, Tianyi Cui, Milos Hauskrecht:
Monitoring ICU Mortality Risk with a Long Short-Term Memory Recurrent Neural Network. PSB 2020: 103-114
2010 – 2019
- 2019
- [j17]Andrew J. King
Using machine learning to selectively highlight patient information. J. Biomed. Informatics 100 (2019) - [c95]Yanbing Xue, Milos Hauskrecht:
Active Learning of Multi-Class Classification Models from Ordered Class Sets. AAAI 2019: 5589-5596 - [c94]Jeongmin Lee
Recent Context-Aware LSTM for Clinical Event Time-Series Prediction. AIME 2019: 13-23 - [c93]Seyedsalim Malakouti, Milos Hauskrecht:
Predicting Patient's Diagnoses and Diagnostic Categories from Clinical-Events in EHR Data. AIME 2019: 125-130 - [c92]Matteo Mantovani
Mining Compact Predictive Pattern Sets Using Classification Model. AIME 2019: 386-396 - [c91]Salim Malakouti, Milos Hauskrecht:
Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification. BIBM 2019: 701-706 - [c90]Siqi Liu, Milos Hauskrecht:
Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. NeurIPS 2019: 1062-1072 - [c89]Zhipeng Luo, Milos Hauskrecht:
Region-Based Active Learning with Hierarchical and Adaptive Region Construction. SDM 2019: 441-449 - [i21]Siqi Liu, Milos Hauskrecht:
Contextual Outlier Detection in Continuous-Time Event Sequences. CoRR abs/1912.09522 (2019) - 2018
- [j16]Siqi Liu, Adam Wright
Change-point detection method for clinical decision support system rule monitoring. Artif. Intell. Medicine 91: 49-56 (2018) - [c88]Andrew J. King, Gregory F. Cooper, Harry Hochheiser, Gilles Clermont, Milos Hauskrecht, Shyam Visweswaran:
Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System. AMIA 2018 - [c87]Yanbing Xue, Milos Hauskrecht:
Active Learning of Multi-Class Classifiers with Auxiliary Probabilistic Information. FLAIRS 2018: 158-163 - [c86]Charmgil Hong, Milos Hauskrecht:
Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data. FLAIRS 2018: 176-179 - [c85]Zhipeng Luo, Milos Hauskrecht:
Hierarchical Active Learning with Group Proportion Feedback. IJCAI 2018: 2532-2538 - [c84]Zhipeng Luo, Milos Hauskrecht:
Hierarchical Active Learning with Proportion Feedback on Regions. ECML/PKDD (2) 2018: 464-480 - [c83]Zitao Liu, Yan Yan, Milos Hauskrecht:
A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic. SIGIR 2018: 889-892 - 2017
- [c82]Siqi Liu, Adam Wright
Change-Point Detection Method for Clinical Decision Support System Rule Monitoring. AIME 2017: 126-135 - [c81]Siqi Liu, Adam Wright
Change-point detection for monitoring clinical decision support systems with a multi-process dynamic linear model. BIBM 2017: 569-572 - [c80]Zitao Liu, Milos Hauskrecht:
A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection. CIKM 2017: 1169-1177 - [c79]Siqi Liu, Adam Wright, Milos Hauskrecht:
Online Conditional Outlier Detection in Nonstationary Time Series. FLAIRS 2017: 86-91 - [c78]Zhipeng Luo, Milos Hauskrecht:
Group-Based Active Learning of Classification Models. FLAIRS 2017: 92-97 - [c77]Yanbing Xue, Milos Hauskrecht:
Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking. FLAIRS 2017: 164-169 - [c76]Adam Wright
Methods for Detecting Malfunctions in Clinical Decision Support Systems. MedInfo 2017: 1385 - [c75]Yanbing Xue, Milos Hauskrecht:
Active Learning of Classification Models with Likert-Scale Feedback. SDM 2017: 28-35 - [i20]Charmgil Hong, Siqi Liu, Milos Hauskrecht:
Detection of Abnormal Input-Output Associations. CoRR abs/1708.01035 (2017) - 2016
- [j15]Milos Hauskrecht, Iyad Batal, Charmgil Hong, Quang Nguyen, Gregory F. Cooper, Shyam Visweswaran, Gilles Clermont:
Outlier-based detection of unusual patient-management actions: An ICU study. J. Biomed. Informatics 64: 211-221 (2016) - [j14]Iyad Batal, Gregory F. Cooper, Dmitriy Fradkin, James H. Harrison Jr., Fabian Moerchen, Milos Hauskrecht:
An efficient pattern mining approach for event detection in multivariate temporal data. Knowl. Inf. Syst. 46(1): 115-150 (2016) - [c74]Zitao Liu, Milos Hauskrecht:
Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data. AAAI 2016: 1273-1279 - [c73]Charmgil Hong, Milos Hauskrecht:
Multivariate Conditional Outlier Detection and Its Clinical Application. AAAI 2016: 4216-4217 - [c72]Yanbing Xue, Milos Hauskrecht:
Learning of Classification Models from Noisy Soft-Labels. ECAI 2016: 1618-1619 - [c71]Zitao Liu, Milos Hauskrecht:
Learning Linear Dynamical Systems from Multivariate Time Series: A Matrix Factorization Based Framework. SDM 2016: 810-818 - [i19]Charmgil Hong, Milos Hauskrecht:
Detecting Unusual Input-Output Associations in Multivariate Conditional Data. CoRR abs/1612.07374 (2016) - 2015
- [j13]Zitao Liu, Milos Hauskrecht:
Clinical time series prediction: Toward a hierarchical dynamical system framework. Artif. Intell. Medicine 65(1): 5-18 (2015) - [c70]Zitao Liu, Milos Hauskrecht:
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis. AAAI 2015: 1798-1804 - [c69]Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht:
Obtaining Well Calibrated Probabilities Using Bayesian Binning. AAAI 2015: 2901-2907 - [c68]Charmgil Hong, Milos Hauskrecht:
Multivariate Conditional Anomaly Detection and Its Clinical Application. AAAI 2015: 4239-4240 - [c67]Eric Heim, Milos Hauskrecht:
Sparse multidimensional patient modeling using auxiliary confidence labels. BIBM 2015: 331-336 - [c66]Zitao Liu, Yan Yan, Jian Yang, Milos Hauskrecht:
Missing Value Estimation for Hierarchical Time Series: A Study of Hierarchical Web Traffic. ICDM 2015: 895-900 - [c65]Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht:
Binary Classifier Calibration Using a Bayesian Non-Parametric Approach. SDM 2015: 208-216 - [c64]Eric Heim, Matthew Berger, Lee M. Seversky, Milos Hauskrecht:
Efficient Online Relative Comparison Kernel Learning. SDM 2015: 271-279 - [c63]Charmgil Hong, Iyad Batal, Milos Hauskrecht:
A Generalized Mixture Framework for Multi-label Classification. SDM 2015: 712-720 - [i18]Eric Heim, Matthew Berger, Lee M. Seversky, Milos Hauskrecht:
Efficient Online Relative Comparison Kernel Learning. CoRR abs/1501.01242 (2015) - [i17]Charmgil Hong, Milos Hauskrecht:
MCODE: Multivariate Conditional Outlier Detection. CoRR abs/1505.04097 (2015) - [i16]Eric Heim, Milos Hauskrecht:
Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels. CoRR abs/1507.07955 (2015) - [i15]Eric Heim, Matthew Berger, Lee M. Seversky, Milos Hauskrecht:
Active Perceptual Similarity Modeling with Auxiliary Information. CoRR abs/1511.02254 (2015) - 2014
- [j12]Quang Nguyen, Hamed Valizadegan, Milos Hauskrecht:
Learning classification models with soft-label information. J. Am. Medical Informatics Assoc. 21(3): 501-508 (2014) - [c62]Adam Wright, Francine L. Maloney, Rachel B. Ramoni, Milos Hauskrecht, Peter J. Embí, Pamela M. Neri, Dean F. Sittig, David W. Bates:
Identifying Clinical Decision Support Failures using Change-point Detection. AMIA 2014 - [c61]Charmgil Hong, Iyad Batal, Milos Hauskrecht:
A Mixtures-of-Trees Framework for Multi-Label Classification. CIKM 2014: 211-220 - [c60]Eric Heim, Hamed Valizadegan, Milos Hauskrecht:
Relative Comparison Kernel Learning with Auxiliary Kernels. ECML/PKDD (1) 2014: 563-578 - [c59]Mahdi Pakdaman Naeini, Iyad Batal, Zitao Liu, Charmgil Hong, Milos Hauskrecht:
An Optimization-based Framework to Learn Conditional Random Fields for Multi-label Classification. SDM 2014: 992-1000 - [i14]Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht:
Binary Classifier Calibration: Bayesian Non-Parametric Approach. CoRR abs/1401.2955 (2014) - [i13]Mahdi Pakdaman Naeini, Gregory F. Cooper, Milos Hauskrecht:
Binary Classifier Calibration: Non-parametric approach. CoRR abs/1401.3390 (2014) - [i12]Charmgil Hong, Iyad Batal, Milos Hauskrecht:
A Mixtures-of-Experts Framework for Multi-Label Classification. CoRR abs/1409.4698 (2014) - 2013
- [j11]Milos Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont:
Outlier detection for patient monitoring and alerting. J. Biomed. Informatics 46(1): 47-55 (2013) - [j10]Hamed Valizadegan, Quang Nguyen, Milos Hauskrecht:
Learning classification models from multiple experts. J. Biomed. Informatics 46(6): 1125-1135 (2013) - [j9]Iyad Batal, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht:
A temporal pattern mining approach for classifying electronic health record data. ACM Trans. Intell. Syst. Technol. 4(4): 63:1-63:22 (2013) - [c58]Milos Hauskrecht, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont:
Conditional Outlier Approach for Detection of Unusual Patient Care Actions. AAAI (Late-Breaking Developments) 2013 - [c57]Zitao Liu, Milos Hauskrecht:
Clinical Time Series Prediction with a Hierarchical Dynamical System. AIME 2013: 227-237 - [c56]Milos Hauskrecht, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont:
Data-driven identification of unusual clinical actions in the ICU. AMIA 2013 - [c55]Iyad Batal, Charmgil Hong, Milos Hauskrecht:
An efficient probabilistic framework for multi-dimensional classification. CIKM 2013: 2417-2422 - [c54]Milos Hauskrecht, Zitao Liu, Lei Wu:
Modeling Clinical Time Series Using Gaussian Process Sequences. SDM 2013: 623-631 - [c53]Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht:
The Bregman Variational Dual-Tree Framework. UAI 2013 - [i11]Milos Hauskrecht, Eli Upfal:
A Clustering Approach to Solving Large Stochastic Matching Problems. CoRR abs/1301.2277 (2013) - [i10]Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling, Thomas L. Dean, Craig Boutilier:
Hierarchical Solution of Markov Decision Processes using Macro-actions. CoRR abs/1301.7381 (2013) - [i9]Eric Heim, Hamed Valizadegan, Milos Hauskrecht:
Relative Comparison Kernel Learning with Auxiliary Kernels. CoRR abs/1309.0489 (2013) - [i8]Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht:
The Bregman Variational Dual-Tree Framework. CoRR abs/1309.6812 (2013) - [i7]Zitao Liu, Milos Hauskrecht:
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series. CoRR abs/1311.7071 (2013) - 2012
- [c52]Hamed Valizadegan, Quang Nguyen, Milos Hauskrecht:
Learning Medical Diagnosis Models from Multiple Experts. AMIA 2012 - [c51]Shuguang Wang, Milos Hauskrecht:
Keyword annotation of biomedicai documents with graph-based similarity methods. BIBM 2012: 1-4 - [c50]Yuriy Sverchkov, Shyam Visweswaran, Gilles Clermont, Milos Hauskrecht, Gregory F. Cooper:
A multivariate probabilistic method for comparing two clinical datasets. IHI 2012: 795-800 - [c49]Iyad Batal, Dmitriy Fradkin, James H. Harrison Jr., Fabian Moerchen, Milos Hauskrecht:
Mining recent temporal patterns for event detection in multivariate time series data. KDD 2012: 280-288 - [c48]Iyad Batal, Gregory F. Cooper, Milos Hauskrecht:
A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules. ECML/PKDD (2) 2012: 260-276 - [c47]Hamed Valizadegan, Saeed Amizadeh, Milos Hauskrecht:
Sampling Strategies to Evaluate the Performance of Unknown Predictors. SDM 2012: 494-505 - [c46]Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht:
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation. UAI 2012: 64-73 - [c45]Saeed Amizadeh, Hamed Valizadegan, Milos Hauskrecht:
Factorized Diffusion Map Approximation. AISTATS 2012: 37-46 - [i6]Branislav Kveton, Milos Hauskrecht:
Partitioned Linear Programming Approximations for MDPs. CoRR abs/1206.3266 (2012) - [i5]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Continuous and Discrete Variables. CoRR abs/1207.4150 (2012) - [i4]Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht:
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation. CoRR abs/1210.4846 (2012) - [i3]Milos Hauskrecht, Tomás Singliar:
Monte-Carlo optimizations for resource allocation problems in stochastic network systems. CoRR abs/1212.2481 (2012) - 2011
- [c44]Iyad Batal, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht:
A Pattern Mining Approach for Classifying Multivariate Temporal Data. BIBM 2011: 358-365 - [c43]Quang Nguyen, Hamed Valizadegan, Milos Hauskrecht:
Learning Classification with Auxiliary Probabilistic Information. ICDM 2011: 477-486 - [c42]Michal Valko, Branislav Kveton, Hamed Valizadegan, Gregory F. Cooper, Milos Hauskrecht:
Conditional Anomaly Detection with Soft Harmonic Functions. ICDM 2011: 735-743 - [c41]Saeed Amizadeh, Shuguang Wang, Milos Hauskrecht:
An Efficient Framework for Constructing Generalized Locally-Induced Text Metrics. IJCAI 2011: 1159-1164 - [c40]Dave Krebs, Alexander Conrad, Milos Hauskrecht, Jingtao Wang:
MARBLS: a visual environment for building clinical alert rules. UIST (Adjunct Volume) 2011: 67-68 - [i2]Milos Hauskrecht:
Value-Function Approximations for Partially Observable Markov Decision Processes. CoRR abs/1106.0234 (2011) - [i1]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Hybrid State and Action Variables. CoRR abs/1110.0028 (2011) - 2010
- [j8]Tomás Singliar, Milos Hauskrecht:
Learning to detect incidents from noisily labeled data. Mach. Learn. 79(3): 335-354 (2010) - [j7]Richard Pelikan, Milos Hauskrecht:
Efficient Peak-Labeling Algorithms for Whole-Sample Mass Spectrometry Proteomics. IEEE ACM Trans. Comput. Biol. Bioinform. 7(1): 126-137 (2010) - [c39]Saeed Amizadeh, Milos Hauskrecht:
Latent Variable Model for Learning in Pairwise Markov Networks. AAAI 2010: 382-387 - [c38]Iyad Batal, Milos Hauskrecht:
Constructing classification features using minimal predictive patterns. CIKM 2010: 869-878 - [c37]Michal Valko, Milos Hauskrecht:
Feature importance analysis for patient management decisions. MedInfo 2010: 861-865 - [c36]Iyad Batal, Milos Hauskrecht:
A Concise Representation of Association Rules Using Minimal Predictive Rules. ECML/PKDD (1) 2010: 87-102 - [c35]Shuguang Wang, Milos Hauskrecht:
Effective query expansion with the resistance distance based term similarity metric. SIGIR 2010: 715-716
2000 – 2009
- 2009
- [c34]Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos Hauskrecht:
A Temporal Abstraction Framework for Classifying Clinical Temporal Data. AMIA 2009 - [c33]Iyad Batal, Milos Hauskrecht:
Boosting KNN text classification accuracy by using supervised term weighting schemes. CIKM 2009: 2041-2044 - [c32]Iyad Batal, Lucia Sacchi, Riccardo Bellazzi, Milos Hauskrecht:
Multivariate Time Series Classification with Temporal Abstractions. FLAIRS 2009 - [c31]Shuguang Wang, Milos Hauskrecht:
Improving Biomedical Document Retrieval by Mining Domain Knowledge. FLAIRS 2009 - [c30]Shuguang Wang, Shyam Visweswaran, Milos Hauskrecht:
Document Retrieval using a Probabilistic Knowledge Model. KDIR 2009: 26-33 - [c29]Iyad Batal, Milos Hauskrecht:
A Supervised Time Series Feature Extraction Technique Using DCT and DWT. ICMLA 2009: 735-739 - 2008
- [c28]Michal Valko, Milos Hauskrecht:
Distance Metric Learning for Conditional Anomaly Detection. FLAIRS 2008: 684-689 - [c27]Tomás Singliar, Milos Hauskrecht:
Approximation Strategies for Routing in Stochastic Dynamic Networks. ISAIM 2008 - [c26]Shuguang Wang, Milos Hauskrecht:
Improving biomedical document retrieval using domain knowledge. SIGIR 2008: 785-786 - [c25]Branislav Kveton, Milos Hauskrecht:
Partitioned Linear Programming Approximations for MDPs. UAI 2008: 341-348 - 2007
- [j6]Richard Pelikan, William L. Bigbee, David Malehorn, James Lyons-Weiler
Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models. Bioinform. 23(22): 3065-3072 (2007) - [c24]Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory F. Cooper:
Evidence-based Anomaly Detection in Clinical Domains. AMIA 2007 - [c23]Tomás Singliar, Milos Hauskrecht:
Modeling Highway Traffic Volumes. ECML 2007: 732-739 - [c22]Tomás Singliar, Milos Hauskrecht:
Learning to Detect Adverse Traffic Events from Noisily Labeled Data. PKDD 2007: 236-247 - 2006
- [j5]Branislav Kveton, Milos Hauskrecht, Carlos Guestrin:
Solving Factored MDPs with Hybrid State and Action Variables. J. Artif. Intell. Res. 27: 153-201 (2006) - [j4]Tomás Singliar, Milos Hauskrecht:
Noisy-OR Component Analysis and its Application to Link Analysis. J. Mach. Learn. Res. 7: 2189-2213 (2006) - [c21]Branislav Kveton, Milos Hauskrecht:
Learning Basis Functions in Hybrid Domains. AAAI 2006: 1161-1166 - [c20]Branislav Kveton, Milos Hauskrecht:
Solving Factored MDPs with Exponential-Family Transition Models. ICAPS 2006: 114-120 - [c19]Daniel Mossé, Louise K. Comfort, Ahmed Amer, José Carlos Brustoloni, Panos K. Chrysanthis
Secure-CITI Critical Information-Technology Infrastructure. DG.O 2006: 253-254 - [c18]Milos Hauskrecht:
Approximate Linear Programming for Solving Hybrid Factored MDPs. AI&M 2006 - 2005
- [c17]Branislav Kveton, Milos Hauskrecht:
An MCMC Approach to Solving Hybrid Factored MDPs. IJCAI 2005: 1346-1351 - [c16]Tomás Singliar, Milos Hauskrecht:
Variational Learning for Noisy-OR Component Analysis. SDM 2005: 370-379 - 2004
- [c15]Branislav Kveton, Milos Hauskrecht:
Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes. ICAPS 2004: 306-314 - [c14]Xinghua Lu, Milos Hauskrecht, Roger S. Day:
Modeling Cellular Processes with Variational Bayesian Cooperative Vector Quantizer. Pacific Symposium on Biocomputing 2004: 533-544 - [c13]Carlos Guestrin, Milos Hauskrecht, Branislav Kveton:
Solving Factored MDPs with Continuous and Discrete Variables. UAI 2004: 235-242 - 2003
- [c12]Milos Hauskrecht, Branislav Kveton:
Linear Program Approximations for Factored Continuous-State Markov Decision Processes. NIPS 2003: 895-902 - [c11]Milos Hauskrecht, Tomás Singliar:
Monte-Carlo optimizations for resource allocation problems in stochastic network systems. UAI 2003: 305-312 - 2001
- [j3]Milos Hauskrecht, Luis E. Ortiz
Efficient Methods for Computing Investment Strategies for Multi-Market Commodity Trading. Appl. Artif. Intell. 15(5): 429-452 (2001) - [c10]Milos Hauskrecht, Eli Upfal:
A Clustering Approach to Solving Large Stochastic Matching Problems. UAI 2001: 219-226 - 2000
- [j2]Milos Hauskrecht, Hamish S. F. Fraser:
Planning treatment of ischemic heart disease with partially observable Markov decision processes. Artif. Intell. Medicine 18(3): 221-244 (2000) - [j1]Milos Hauskrecht:
Value-Function Approximations for Partially Observable Markov Decision Processes. J. Artif. Intell. Res. 13: 33-94 (2000) - [c9]Milos Hauskrecht, Luis E. Ortiz, Ioannis Tsochantaridis, Eli Upfal:
Computing Global Strategies for Multi-Market Commodity Trading. AIPS 2000: 159-166
1990 – 1999
- 1999
- [c8]Milos Hauskrecht, Gopal Pandurangan, Eli Upfal:
Computing Near Optimal Strategies for Stochastic Investment Planning Problems. IJCAI 1999: 1310-1315 - 1998
- [c7]Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, Leonid Peshkin, Leslie Pack Kaelbling, Thomas L. Dean, Craig Boutilier:
Solving Very Large Weakly Coupled Markov Decision Processes. AAAI/IAAI 1998: 165-172 - [c6]Milos Hauskrecht, Hamish Fraser:
Modeling treatment of ischemic heart disease with partially observable Markov decision processes. AMIA 1998 - [c5]Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling, Thomas L. Dean, Craig Boutilier:
Hierarchical Solution of Markov Decision Processes using Macro-actions. UAI 1998: 220-229 - 1997
- [b1]Milos Hauskrecht:
Planning and control in stochastic domains with imperfect information. Massachusetts Institute of Technology, Cambridge, MA, USA, 1997 - [c4]Milos Hauskrecht:
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes. AAAI/IAAI 1997: 734-739 - [c3]Milos Hauskrecht:
Dynamic Decision Making in Stochastic Partially Observable Domains: Ischemic Heart Disease Example. AIME 1997: 296-299 - 1991
- [c2]M. Popper, Milos Hauskrecht:
Declarative and operational in knowledge based systems. MIE 1991: 299-303 - [c1]J. Stanek, M. Popper, Milos Hauskrecht:
The operational aspects of object-oriented approach in medical expert systems design. MIE 1991: 304-308
Coauthor Index
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