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Contributed Papers

Advances with Tree Based Statistical Methods

Mon, Aug 3, 10:30 AM - 12:20 PM Room CC-105 Thomas M. Menino Convention & Exhibition Center

Yifan ChenChair

Section on Statistical Learning and Data Science

6 Presentations

A Centralized Framework for Interpretable Learning via Tree-Based Variable Selection

10:35 AM - 10:50 AM

Paul Gaona Partida

Co-authors: Ana Kenney (UC Irvine)

Decision Trees Pruning L0 Variable Selection Statistical Learning

Decentralized Graph Traversal Planning with Bayesian Agent Modeling

10:50 AM - 11:05 AM

Li Zhou (Auburn University)

Co-authors: Elvan Ceyhan (Auburn University)

Multi-Agent Planning Bayesian Decision Making Adversarial Risk Analysis Network Traversal Reinforcement Learning

Interpretable distillation of tree ensembles with Consensus Tree (CoTree)

11:05 AM - 11:20 AM

Bingxue An (University of Notre Dame)

Co-authors: Tiffany Tang (University of Notre Dame)

tree ensembles tree distance interpretability ensemble distillation

Neural Network-Based Estimation of Subject-Specific Densities from Repeated Measurements

11:20 AM - 11:35 AM

Caihong Qin (Indiana University)

Co-authors: Lan Xue (Oregon State University), See Ling Loy (Duke-NUS Medical School), Roger Zoh (Indiana University), Carmen Tekwe (Indiana University)

Density estimation Neural networks Measurement error Wearable data

Randomized Trees with Conditional Inference

11:35 AM - 11:50 AM

Soham Bakshi (University of Michigan)

Co-authors: Snigdha Panigrahi (University of Michigan)

Algorithmic model Classification trees Exponential mechanism Gumbel softmax Randomization Selective inference

Tree-Based Predictive Models for Noisy Input Data

11:50 AM - 12:05 PM

Kevin McCoy (Rice University)

Co-authors: Zachary Wooten (St. Jude Children's Research Hospital), Christine Peterson (University of Texas MD Anderson Cancer Center)

decision trees Bayesian additive regression trees measurement error uncertainty quantification latent variable model Bayesian hierarchical modeling

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