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353
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Tue, 7/30/2019,
10:30 AM -
11:15 AM
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CC-Hall C
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SPEED: Statistical Learning and Data Science Speed Session 2, Part 2 — Contributed Poster Presentations
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Section on Statistical Learning and Data Science, Text Analysis Interest Group
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Chair(s): Ali Shojaie, University of Washington
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Oral Presentations
for this session.
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1:
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Three-Dimensional Radial Visualization of High-Dimensional Continuous or Discrete Data
Yifan Zhu, Iowa State University; Fan Dai, Iowa State University; Ranjan Maitra, Iowa State University
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3:
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An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data
Warren Keil; Tyler Cook, University of Central Oklahoma
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4:
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Diagnostic Accuracy Evaluation of Diagnostic Assessment Model in Longitudinal Data: a Simulation Study of Neural Network Approach
Chi Chang, Michigan State University; Harlan McCaffery, University of Michigan
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5:
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Smoothing Random Forest
Benjamin LeRoy, Carnegie Mellon University; Max G'Sell, Carnegie Mellon University
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6:
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Aggregated Pairwise Classification of Statistical Shapes
Min Ho Cho, The Ohio State University
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7:
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Statistical Optimality of Interpolated Nearest Neighbor Algorithms
Yue Xing, Purdue University; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
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8:
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Ground Truth? Understanding How Humans Label Records and the Impact of Uncertainty
Kayla Frisoli, Carnegie Mellon University; Rebecca Nugent, Carnegie Mellon University
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9:
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Block-Wise Partitioning for Extreme Multi-Label Classification
Yuefeng Liang, UC Davis; Thomas C. M. Lee, UC Davis; Cho-Jui Hsieh, UCLA
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10:
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A Statistical Model for Tropical Cyclone Genesis and Assessing Its Differences Between Basins and Climates
Arturo Fernandez, University of California - Berkeley
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11:
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Discovery of Gene Regulatory Networks Using Adaptively Selected Gene Perturbation Experiments
Michele Zemplenyi, Harvard University; Jeffrey Miller, Harvard TH Chan School of Public Health
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12:
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Stagewise Generalized Estimating Equations for Varying Coefficient Models
Gregory Vaughan, Bentley University; Yicheng Kang, Bentley University
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13:
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Stacked Ensemble Learning for Propensity Score Methods in Observational Studies
Maximilian Autenrieth, San Diego State University and Ulm University; Richard Levine, San Diego State University; Juanjuan Fan, San Diego State University; Maureen Guarcello, San Diego State University
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14:
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Predicting Sub-Cellular Location of Plant Protein Using Supervised Machine Learning
David Arthur; Benjamin Annan, Youngstown State University; Eric Quayson, Youngstown State University; Jack Min, Youngstown State University; Guang-Hwa Andy Chang, Youngstown State University
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15:
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Semi-Supervised, Dynamic Class-Informative Feature Learning
Vincent Pisztora
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