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498
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Thu, 8/6/2020,
10:00 AM -
2:00 PM
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Virtual
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Modern Machine Learning — Contributed Papers
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Section on Statistical Learning and Data Science
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Chair(s): Rebecca North, North Carolina State University
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Random Forest Kernels: Utility and Insights for Interpretable Statistical Learning
Dai Feng, AbbVie; Richard Baumgartner, Merck
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Uniform Regret Bounds for Quantile Regression Tree Process in Offline and Online Settings
Fei Fang, Duke University; Alexandre Belloni, Duke University
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Deep Learning with Gaussian Differential Privacy
Zhiqi Bu, University of Pennsylvania
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An Optimal Statistical and Computational Framework for Generalized Tensor Estimation
Rungang Han, University of Wisconsin-Madison; Rebecca Willett, University of Chicago; Anru Zhang, University of Wisconsin-Madison
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Nonparametric Individual Treatment Effect Estimation for Survival Data with Random Forests
Denis Larocque, HEC Montreal; Sami Tabib, HEC Montreal
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Sequential Changepoint Detection for Classifier Label Shift
Ciaran Evans, Carnegie Mellon University; Max G'Sell, Carnegie Mellon University
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Machine Learning Oracle to Guide Statistical Data Processing
Lucas Koepke, National Institute of Standards and Techology; Michael Frey, National Institute of Standards and Technology
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