Johannes Lederer

Data Science & Artificial Intelligence

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Category Archives: New Paper

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Posted by

LedererLab

Posted on

September 26, 2016

Posted under

New Paper

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! 🙂

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Posted by

LedererLab

Posted on

September 20, 2016

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New Paper

Graphical Models for Discrete and Continuous Data

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.

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Posted by

LedererLab

Posted on

August 3, 2016

Posted under

New Paper

Oracle Inequalities for High-dimensional Prediction

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.

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