We have established risk bounds for robust deep learning here.
Author Archives: LedererLab
Layer sparsity in neural networks
We have put a new paper about layer sparsity in neural networks on arXiv.
Inference in Labor Economics
We have now put our paper “A pipeline for variable selection and false discovery rate control with an application in labor economics” on arXiv. The paper will be part of the Annual Congress of the Swiss Society of Economics and Statistics in 2021. Congratulations Sophie-Charlotte!
High-dimensional inference
Our paper “Inference for high-dimensional instrumental variables regression” has now been published. Congratulations again to David and Jing!
Statistical Guarantees for Deep Learning
We have derived statistical guarantees for deep learning here. Well done, Mahsa and Fang! ⚡⚡⚡
Calibrating the Graphical Lasso
We have established a new strategy for calibrating the graphical lasso here. Great work, Mike! 🍷
Lasso in Theory and Practice
We have uploaded a new paper on the lasso’s effective noise and on consequences for calibration and inference here. Thanks to Michael for the great collaboration!
FDR Control in Labor Economics
Our paper A Pipeline for Variable Selection and False Discovery Rate Control with an Application in Labor Economics has been accepted for presentation at the 2020 Annual Congress of the Swiss Society of Economics and Statistics. Congratulations to Sophie-Charlotte Klose! Very impressive!
Ridge Regression Without Tuning Parameters
We have uploaded a paper on tuning-free ridge regression here. Well done, Shih-Ting and Fang!
High-dimensional Inference
Our paper Inference for high-dimensional instrumental variables regression has been accepted by the Journal of Econometrics. Wonderful work from my co-authors Jing (faculty at University of Washington) and David (former student at University of Washington)! ✈
