We have established risk bounds for robust deep learning here.
We have put a new paper about layer sparsity in neural networks on arXiv.
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!
Our paper “Inference for high-dimensional instrumental variables regression” has now been published. Congratulations again to David and Jing!
We have derived statistical guarantees for deep learning here. Well done, Mahsa and Fang! ⚡⚡⚡
We have established a new strategy for calibrating the graphical lasso here. Great work, Mike! 🍷
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!
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!
We have uploaded a paper on tuning-free ridge regression here. Well done, Shih-Ting and Fang!
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)! ✈