Recent Advances in High-Dimensional Machine Learning and Inference
Category: International Statistical Institute
Abstract
To address the aforementioned fundamental challenges, this invited session brings together four experts who will introduce some cutting-edge developments on these interrelated topics from unique perspectives. A common theme of the session is high-dimensional machine learning and inference.
Session organizer: Jinchi Lv , University of Southern California
Session chair: Jinchi Lv
List of session speakers (please arrange the talks in the order specified below):
1) Jianqing Fan , Princeton University
Talk title: Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression
2) Yingying Fan , University of Southern California
Talk title: FACT: High-Dimensional Random Forests Inference
3) Timothy Cannings , University of Edinburgh
Talk title: Minimax Optimal Classification under Missing Data
4) Xiaowu Dai , University of California, Los Angeles
Talk title: Orthogonalized Kernel Debiased Machine Learning