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2020 – today
- 2022
[i24]Jeong Min Lee, Milos Hauskrecht:
Learning to Adapt Clinical Sequences with Residual Mixture of Experts. CoRR abs/2204.02687 (2022)- 2021
- [j18]Jeong Min Lee, Milos Hauskrecht:
Modeling multivariate clinical event time-series with recurrent temporal mechanisms. Artif. Intell. Medicine 112: 102021 (2021) - [c104]Jeong Min Lee
, Milos Hauskrecht:
Neural Clinical Event Sequence Prediction Through Personalized Online Adaptive Learning. AIME 2021: 175-186 - [c103]Matthew Barren, Milos Hauskrecht:
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 Conference 2021 - [c101]Siqi Liu, Milos Hauskrecht:
Event Outlier Detection in Continuous Time. ICML 2021: 6793-6803 - [i23]Jeong Min 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]Jeong Min 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]Jeong Min Lee, Milos Hauskrecht:
Clinical Event Time-Series Modeling with Periodic Events. FLAIRS Conference 2020: 94-99 - [c97]Salim Malakouti, Milos Hauskrecht:
Not All Samples are Equal: Class Dependent Hierarchical Multi-Task Learning for Patient Diagnosis Classification. FLAIRS Conference 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, Gregory F. Cooper, Gilles Clermont, Harry Hochheiser, Milos Hauskrecht, Dean F. Sittig, Shyam Visweswaran:
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]Jeong Min Lee, Milos Hauskrecht:
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, Carlo Combi, Milos Hauskrecht:
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, Milos Hauskrecht:
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 Conference 2018: 158-163 - [c86]Charmgil Hong, Milos Hauskrecht:
Multivariate Conditional Outlier Detection: Identifying Unusual Input-Output Associations in Data. FLAIRS Conference 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, Milos Hauskrecht:
Change-Point Detection Method for Clinical Decision Support System Rule Monitoring. AIME 2017: 126-135 - [c81]Siqi Liu, Adam Wright, Dean F. Sittig, Milos Hauskrecht:
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 Conference 2017: 86-91 - [c78]Zhipeng Luo, Milos Hauskrecht:
Group-Based Active Learning of Classification Models. FLAIRS Conference 2017: 92-97 - [c77]Yanbing Xue, Milos Hauskrecht:
Efficient Learning of Classification Models from Soft-label Information by Binning and Ranking. FLAIRS Conference 2017: 164-169 - [c76]Adam Wright, Trang T. Hickman, Dustin McEvoy, Skye Aaron, Angela Ai, Joan S. Ash, Jan Marie Andersen, Rachel Badovinac Ramoni, Milos Hauskrecht, Peter J. Embí, Richard Schreiber, Dean F. Sittig, David W. Bates:
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 - [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 Conference 2009 - [c31]Shuguang Wang, Milos Hauskrecht:
Improving Biomedical Document Retrieval by Mining Domain Knowledge. FLAIRS Conference 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 Conference 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, Milos Hauskrecht:
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]