Johannes Lederer

Statistics, Machine Learning & Data Science

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Category Archives: New Paper

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LedererLab

Posted on

September 26, 2019

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New Paper

Personalized Medicine

We have uploaded a new paper on prediction in personalized medicine here. Congratulations to Shih-Ting and Yannick, who are the student authors of this paper!

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Posted by

LedererLab

Posted on

July 10, 2019

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New Paper

FDR Control in Regression and Graphical Modeling

We have uploaded two new papers on FDR control here and here on arXiv. Great job, Fang, Lu, and Tobias!

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LedererLab

Posted on

January 6, 2018

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New Paper

Prediction Error Bounds for Linear Regression With the TREX

We have uploaded a new paper that contains theoretical insights about our TREX here on arXiv. Thanks to my collaborators Jacob, Irina, and Christian!

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Posted by

LedererLab

Posted on

October 10, 2017

Posted under

New Paper

Prediction With Maximum Regularized Likelihood Estimators

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! ✌

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Posted by

LedererLab

Posted on

September 16, 2017

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New Paper

Inference for high-dimensional nested regression

We have established inference for two-stage regression models, with both stages high-dimensional. The paper is available here. Awesome work, David! ⛰

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Posted by

LedererLab

Posted on

April 18, 2017

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New Paper

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 here. Well done, Yunqi! 😁

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Posted by

LedererLab

Posted on

October 4, 2016

Posted under

New Paper

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! 😎

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Johannes Lederer
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