Keyword Search
Sessions Were Renumbered as of May 19.
Legend:
CC-W = McCormick Place Convention Center, West Building, CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago, UC= Conference Chicago at University Center
* = applied session ! = JSM meeting theme
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Keyword Search Criteria: Machine Learning returned 48 record(s)
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Monday, 08/01/2016
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What Can Statistics Learn from Machine Learning? And Vice Versa?
Edward Henry Kennedy, Carnegie Mellon University; Ryan Tibshirani, Carnegie Mellon University
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Sufficient Markov Decision Processes
Longshaokan Wang, North Carolina State University
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Machine-Learning Tools for Finding Biomarkers in Precision Medicine
Jonathan Hibbard, The University of North Carolina at Chapel Hill; Michael R. Kosorok, The University of North Carolina at Chapel Hill
11:00 AM
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Enabling Privacy Preserving Machine Learning at Scale
Farinaz Koushanfar, UCSD
11:00 AM
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Nonparametric Methods for Doubly Robust Estimation of Continuous Treatment Effects
Edward Kennedy, University of Pennsylvania; Zongming Ma, University of Pennsylvania; Dylan Small, University of Pennsylvania
2:05 PM
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Benchmarking and Assessment for Multiple Imputation
Gerko Vink, Utrecht University
2:05 PM
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Large-Margin Classification with Multiple Decision Rules
Patrick Kimes, Roche Sequencing; Yufeng Liu, The University of North Carolina at Chapel Hill; J. S. Marron, The University of North Carolina at Chapel Hill; David Neil Hayes, The University of North Carolina at Chapel Hill
2:45 PM
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Applications of Machine Learning in Environmetrics: Detecting Dynamic Trend-Based Clusters
Xin Huang, The University of Texas at Dallas; Iliyan R. Iliev, The University of Texas at Dallas; Lyubchich Vyacheslav, University of Maryland Center for Environmental Science; Alexander Brenning , University of Jena; Yulia R. Gel, The University of Texas at Dallas
3:35 PM
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Tuesday, 08/02/2016
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Machine Learning Applications for Survey Design, Collection, and Adjustment: Going Beyond the Trees to See Clusters, Forests, and Neighbors
Trent Buskirk, Marketing Systems Group
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ZIP Codes and Neural Networks: Machine Learning for Handwritten Number Recognition
Cuixian Chen, The University of North Carolina at Wilmington; Taylor Harbold, The University of North Carolina at Wilmington; Courtney Rasmussen, The University of North Carolina at Wilmington; Michelle Page, The University of North Carolina at Wilmington
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Machine Learning for Exploratory Analyses of Psychological Data
Gitta Lubke
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Employing Machine Learning Approaches in Social Scientific Analyses
Arne Bethmann, Institute for Employment Research; Jonas Beste, Institute for Employment Research
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Improved Disease Burden Modeling from Administrative Health Care Data
Ralph (PhD Student) Ward , Medical University of South Carolina; Mulugeta Gebregziabher, Medical University of South Carolina; Leonard Egede, Health Equity and Rural Outreach Innovation Center; Lewis Frey, Medical University of South Carolina; Viswanathan Ramakrishnan, Medical University of South Carolina; Robert Axon, Medical University of South Carolina
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Statistical Learning Methods for Record Linkage: A Pioneer Mortality Example
Kristina Murri, Brigham Young University
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Predicting Chemical Dose-Response Toxicity Through Chemical Structure Activity Relationships
Matthew Wheeler, CDC/NIOSH
9:15 AM
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Predictive Modeling of Severity of Injuries in Motor Vehicle Crashes
Aditi Pradeep Sharma, University of Maryland Baltimore County; Michael Wierzbicki, The EMMES Corporation; Gaurav Sharma, The EMMES Corporation
9:15 AM
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Communication Over a Noisy Channel Using High-Dimensional Linear Regression with Gaussian Design
Cynthia Rush, Yale University; Adam Greig, University of Cambridge; Ramji Venkataramanan, University of Cambridge
10:05 AM
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Decoding Brain States from fMRI Data with a Machine Learning Method
Elizabeth Chou
10:35 AM
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Computationally Efficient Question Selection in Adaptive Questionnaires
John Riddles, George Mason University; James E. Gentle, George Mason University
10:50 AM
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Machine Learning Methods in High-Dimensional Branching Processes
Anand N. Vidyashankar, George Mason University
10:55 AM
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Statistical Learning Methods for Record Linkage: A Pioneer Mortality Example
Kristina Murri, Brigham Young University
11:05 AM
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Using Machine Learning to Correct for Survey Nonresponse Bias
Curtis Signorino, University of Rochester; Antje Kirchner, University of Nebraska - Lincoln
11:15 AM
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Methodological Strategies to Define a Generalizable Model for Machine Learning Ensemble Techniques
Joel Correa da Rosa, Rockefeller University; Lewis Tomalin, Icahn School of Medicine at Mount Sinai; Mayte Suárez-Fariñas, Icahn School of Medicine at Mount Sinai
11:35 AM
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Maximizing Text Mining Performance: The Impact of Pre-Processing
Dario Gregori, University of Padova; Paola Berchialla, University of Torino; Nicola Soriani, University of Padova; Ileana Baldi, University of Padova; Corrado Lanera, University of Padova
11:55 AM
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A Case Study in Machine Learning Approaches to Survey Nonresponse Adjustments
Minsun Riddles, Westat; Bob Fay, Westat; David McGrath, Defense Manpower Data Center; Eric Falk, Defense Manpower Data Center
11:55 AM
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Being Bayesian in a Big Data World
David Banks, Duke University
2:55 PM
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Wednesday, 08/03/2016
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Members Choice: Hot Topics in Statistical Learning and Data Mining
Glen Wright Colopy, University of Oxford
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Predicting Patient Costs
Grace Shrader, University of Wisconsin - Madison; Jonathan Berthet, The University of Chicago; Katherine Tong, The University of Chicago; David O. Meltzer, The University of Chicago
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Properties of Adaptive Clinical Trial Signature Design in the Presence of Gene and Gene-Treatment Interaction
Alexander Cambon, University of Louisville; Shesh N. Rai, University of Louisville; Guy Brock, University of Louisville
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Time Series Matching for Novelty Detection in the Stepdown Ward: A Gaussian Process Approach
Glen Wright Colopy, University of Oxford; Marco A. F. Pimentel; Stephen J. Roberts; David A. Clifton
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Application of Computer Vision and Machine Learning to Public Health Data Validation
Daniel Robertson, CDC; Jin-Mann Lin, CDC
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Modeling Temporal Dependence to Improve Learning Algorithms for Streaming Data
Maggie Johnson, Iowa State University; Petrutza Caragea, Iowa State University; Lisa Bramer, Pacific Northwest National Laboratory; Bryan Stanfill, Pacific Northwest National Laboratory; Sarah Reehl, Pacific Northwest National Laboratory
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Statistics and Machine Learning in Pharmacovigilance for Signal Detection of Cardiovascular Risks
James Chen, FDA/NCTR; Weizhong Zhao , FDA/NCTR; Wen Zou, FDA/NCTR
9:05 AM
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Doubly Robust Regression Trees Under Competing Risks
Youngjoo Cho, University of Rochester Medical Center; Robert Strawderman, University of Rochester Medical Center
9:35 AM
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Machine Learning and Causality
Guido Imbens, Stanford University
10:35 AM
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Covariate Balancing Propensity Score via Tailored Loss Function
Qingyuan Zhao, Stanford University; Trevor Hastie, Stanford University
11:05 AM
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Using Machine Learning Algorithms for Handling Missingness: Application to Predicting Drug-Disease and Drug-Drug Interactions
Ruoshui Zhai, Brown University; Roee Gutman, Brown University
11:35 AM
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Generalized Difference in Difference Models with Gaussian Processes
William Herlands, Carnegie Mellon University; Daniel B. Neill, Carnegie Mellon University; Akshaya Jha, Carnegie Mellon University; Seth Flaxman, University of Oxford; Kun Zhang, Carnegie Mellon University
2:35 PM
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Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
Edward McFowland, Carlson School of Management; Sriram Somanchi, University of Notre Dame; Daniel B. Neill, Carnegie Mellon University
3:35 PM
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Thursday, 08/04/2016
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Comparison of Some Subgroup Identification Algorithms for Precision Medicine in Drug Development
Xin Huang; Yan Sun, AbbVie; Saptarshi Chatterjee, AbbVie; Viswanath Devanarayan, AbbVie
8:55 AM
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Adaptive Sequential Model Selection
William Fithian, University of California at Berkeley; Jonathan Taylor, Stanford University; Robert Tibshirani, Stanford University; Ryan Tibshirani, Carnegie Mellon University
10:35 AM
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Multicategory Personalized Treatment Rule with Application to Diabetes Data Analysis
Xuanyao He, Eli Lilly and Company; Haoda Fu
10:55 AM
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New Machine-Learning Approaches to Causal Inference
Cynthia Rudin, Duke University
11:25 AM
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