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Keyword Search Criteria: machine learning returned 36 record(s)
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Monday, 07/30/2018
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Predicting Hospital Readmission for Diabetes Patients by Classical and Machine Learning Approaches
Gabrielle LaRosa, University of Pittsburgh; Chathurangi Pathiravsan, Southern Illinois University Carbondale; Rajapaksha Wasala M Anusha Madushani, University of Florida
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Machine Learning with Ensemble Feature Selections for Mass Spectrometry Data in Cancer Study
Yulan Liang, University of Maryland Baltimore; Amin Gharipour, Griffith University; Arpad Kelemen, University of Maryland Baltimore; Adam Kelemen, University of Maryland College Park; Hui Zhang, Johns Hopkins Medical Institutions
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Bayesian and Unsupervised Machine Learning Machines for Jazz Music Analysis
Qiuyi Wu, ASA; Ernest Fokoue, ASA
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Model Class Reliance: Variable Importance Measures for Any Machine Learning Model Class, from the
Aaron Fisher, Harvard University; Cynthia Rudin, Duke University; Francesca Dominici, Harvard T. H. Chan School of Public Health
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New Applications of Machine Learning to Estimating Large Physician Demand Models
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research
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Battle Royale: Machine Learning vs. Mechanistically Motivated Spatio-Temporal Models for Atmospheric and Oceanic Processes
Christopher K. Wikle, University of Missouri
8:35 AM
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Model Class Reliance: Variable Importance Measures for Any Machine Learning Model Class, from the
Aaron Fisher, Harvard University; Cynthia Rudin, Duke University; Francesca Dominici, Harvard T. H. Chan School of Public Health
8:40 AM
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New Applications of Machine Learning to Estimating Large Physician Demand Models
Bryan Sayer, Social & Scientific Systems, Inc.; William Encinosa, Agency for Health Care Quality and Research
10:05 AM
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Edward: a Library for Probabilistic Machine Learning and Statistics
Dustin Tran, Columbia University; David Blei, Columbia University
11:35 AM
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Nonparametric Variable Importance Assessment Using Machine Learning Techniques
Brian Williamson, University of Washington; Peter Gilbert, Fred Hutchinson Cancer Research Center; Noah Simon, University of Washington; Marco Carone, University of Washington
2:25 PM
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Prediction Using Machine Learning Algorithms by Small Sample Size Data
Yan Wang, Field School of Public Health, UCLA; Honghu Liu, UCLA; Jian L Zhang, Kaiser Permanente
3:20 PM
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Tuesday, 07/31/2018
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Interpretable Statistical Machine Learning for Validation and Uncertainty Quantification of Complex Models
Ana Kupresanin, Lawrence Livermore National Laboratory
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An Application of Machine Learning for 3D IC Defect Detection
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University
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A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
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Machine Learning to Evaluate the Quality of Patient Reported Epidemiological Data
Robert L. Wood, Resonate & Wichita State University; Futoshi Yumoto, Resonate; Rochelle Tractenberg, Georgetown University
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Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University
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Machine Learning Methods for Animal Movement
Dhanushi A Wijeyakulasuriya, Pennsylvania State University; Ephraim Hanks, The Pennsylvania State University; Benjamin Shaby, Penn State University
9:35 AM
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Budget-Constrained Feature Selection for Binary Classification: a Neyman-Pearson Approach
Yiling Chen, University of California, Los Angeles; Xin Tong, University of Southern California; Jingyi Li, University of California, Los Angeles
10:05 AM
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Using Genomic Features to Make Smart Clinical Decisions: The Power of Machine Learning with RNA-Seq
Jing Huang, Veracyte Inc; Su yeon Kim , Veracyte Inc; Yangyang Hao, Veracyte Inc; Jing Lu, Veracyte Inc; Joshua Babiarz, Veracyte Inc; Sean Walsh, Veracyte Inc; Giulia Kennedy, Veracyte Inc
11:00 AM
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A Machine Learning (ML) Approach to Prognostic and Predictive Covariate Identification for Subgroup Analysis and Hypotheses Generation
David A James, Novartis
11:35 AM
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An Application of Machine Learning for 3D IC Defect Detection
Meihui Guo, National Sun Yat-Sen University; Yu-Jung Huang, I-Shou University
11:40 AM
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Predicting Overflow: A Novel Application of Latrine Sensors and Machine Learning for Optimizing Sanitation Services in Informal Settlements
Phillip Turman-Bryant, Portland State University; Evan Thomas, Portland State University
11:55 AM
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Machine Learning to Evaluate the Quality of Patient Reported Epidemiological Data
Robert L. Wood, Resonate & Wichita State University; Futoshi Yumoto, Resonate; Rochelle Tractenberg, Georgetown University
12:10 PM
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Causal Inference Using EMRs with Missing Data: a Machine Learning Approach with an Application on the Evaluation of Implantable Cardioverter Defibrillators
Changyu Shen, Beth Israel Deaconess Medical Center, Harvard Medical School; Xiaochun Li, Indiana University; Zuoyi Zhang, Regenstrief Institute; Alfred E Buxton, Beth Israel Deaconess Medical Center
2:25 PM
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The Use of Machine Learning Methods to Improve the US National Resources Inventory Survey
Zhengyuan Zhu, Iowa State University
3:05 PM
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Wednesday, 08/01/2018
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Mortality Prediction with Multiple Unordered Treatments for Aortic Valve Replacement
Samrachana Adhikari, Harvard Medical School; Sherri Rose, Harvard Medical School; Sharon-Lise Normand, Harvard University; Jordan Bloom, Harvard Medical School; David Shahian, Harvard Medical School; Jake Spertus, Harvard Medical School
8:55 AM
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Can We Train Machine Learning Methods to Outperform the High-Dimensional Propensity Score Algorithm?
Mohammad Ehsanul Karim, University of British Columbia; Robert W Platt, McGill University
9:20 AM
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Distributed Machine Learning with H2O
Navdeep Gill, H2O.ai
11:35 AM
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On the Art and Science of Machine Learning Explanations
Patrick Hall, H20.ai
2:05 PM
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Local, Model-Agnostic Explanations of Machine Learning Predictions
Sameer Singh, University of California, Irvine
2:45 PM
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Evaluating the Census Planning Database, MSG, and Paradata as Predictors of Household Propensity to Respond
Xiaoshu Zhu, Westat; Robert Baskin, Westat; David Morganstein, Westat
2:50 PM
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Optimal Bayesian Design for Models with Intractable Likelihoods via Machine Learning Methods
Christopher C Drovandi, Queensland University of Technology; Markus Hainy, QUT
3:05 PM
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