Our paper on tuning parameter calibration for the Lasso has been accepted at JMLR. You can find an updated version here.
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.
Johannes, Jing (UW Econ), and David are awarded the grant “AWS Cloud Credits for Research” sponsored by Amazon! Congratulations especially to David, first year PhD student in the Stats Department and key researcher in the team.
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.
This is the first post on our brand new homepage – hopefully many more to come.
Gina and Kimberly did all the work: thank you for the wonderful job!! 🙂