294
Tue, 7/30/2019,
8:30 AM -
10:20 AM
CC-502
SPEED: Statistical Learning and Data Science Speed Session 2, Part 1 — Contributed Speed
Section on Statistical Learning and Data Science , Text Analysis Interest Group
Chair(s): Sumanta Basu, Cornell University
Poster Presentations
for this session.
8:35 AM
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
8:40 AM
Semi-Supervised, Dynamic Class-Informative Feature Learning
Vincent Pisztora
8:45 AM
An Imputation Approach for Fitting Random Survival Forests with Interval-Censored Survival Data
Warren Keil ; Tyler Cook, University of Central Oklahoma
8:50 AM
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
8:55 AM
Smoothing Random Forest
Benjamin LeRoy, Carnegie Mellon University ; Max G'Sell, Carnegie Mellon University
9:00 AM
Aggregated Pairwise Classification of Statistical Shapes
Min Ho Cho, The Ohio State University
9:05 AM
Statistical Optimality of Interpolated Nearest Neighbor Algorithms
Yue Xing, Purdue University ; Qifan Song, Purdue University; Guang Cheng, Purdue Statistics
9:10 AM
Ground Truth? Understanding How Humans Label Records and the Impact of Uncertainty
Kayla Frisoli, Carnegie Mellon University ; Rebecca Nugent, Carnegie Mellon University
9:15 AM
Block-Wise Partitioning for Extreme Multi-Label Classification
Yuefeng Liang, UC Davis ; Thomas C. M. Lee, UC Davis; Cho-Jui Hsieh, UCLA
9:20 AM
A Statistical Model for Tropical Cyclone Genesis and Assessing Its Differences Between Basins and Climates
Arturo Fernandez, University of California - Berkeley
9:30 AM
Discovery of Gene Regulatory Networks Using Adaptively Selected Gene Perturbation Experiments
Michele Zemplenyi, Harvard University ; Jeffrey Miller, Harvard TH Chan School of Public Health
9:35 AM
Stagewise Generalized Estimating Equations for Varying Coefficient Models
Gregory Vaughan, Bentley University ; Yicheng Kang, Bentley University
9:40 AM
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
9:45 AM
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
9:50 AM
Floor Discussion
