Find our new public GitHub repository here. Currently, it consists of the software emerging from the hdim project.
Integrating Additional Knowledge Into Graph Estimation
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 at here. Well done, Yunqi! 😁
RRF Grant Awarded
Johannes, Jing, and David have been awarded with a grant from the UW Royalty Research Fund (RRF) for their Big Data research in Econometrics. Second hit: it pays to work with Jing and David… 🍸
STAT 582 Upcoming
Important dates and further information about the course STAT 582 have been uploaded in the teaching section. Note the change of location to LOW 101.
A practical scheme for Lasso calibration
Our paper on tuning parameter calibration for the Lasso has been accepted at JMLR. You can find an updated version here.
Tuning Parameter Calibration in High-dimensional Logistic Regression With Theoretical Guarantees
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! 😎
Efficient Feature Selection With Large and High-dimensional Data
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! 🙂
