Johannes Lederer

Statistics, Machine Learning & Data Science

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Category Archives: New Paper

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LedererLab

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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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LedererLab

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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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LedererLab

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October 4, 2016

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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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LedererLab

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September 26, 2016

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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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LedererLab

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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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LedererLab

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August 3, 2016

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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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