We have uploaded a paper titled “Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications” here on arXiv. We discuss “slow” rates for MRLEs in a wide range of settings. Cool stuff, Rui! ✌
We have established inference for two-stage regression models, with both stages high-dimensional. The paper is available here. Awesome work, David! ⛰
We have developed an approach for integrating additional knowledge into parameter estimation in graphical models. The main idea is to funnel the knowledge into the tuning parameters. Find the paper here. Well done, Yunqi! 😁
We have extended the AV-scheme for tuning parameter calibration to logistic regression. Our approach provides efficient feature selection and is supported by theoretical guarantees. The paper can be downloaded here. Great job, Wei! 😎
After presenting it in a couple of talks, we have now submitted the manuscript about efficient feature selection. In particular, we introduce a simple, yet effective algorithm based on Lasso optimization steps. The paper is available here. Congrats to Néhémy! 🙂
We have uploaded a paper on graphical models. In this paper, we introduce a general framework that allows for discrete, continuous, and combined types of data. Find the manuscript here.
We have just uploaded a new paper on high-dimensional prediction. It provides bounds for a wide range of penalized estimators – importantly, without making assumptions on the design matrix. Check it out here.