Publications

From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictive Performance

We develop and evaluate an application-agnostic ML-based data cleaning approach using well-established imputation techniques for automated detection and cleaning of erroneous values. To improve the degree of automation, we combine imputation techniques with conformal prediction (CP), a model-agnostic and distribution-free method to quantify and calibrate the uncertainty of ML models.

Sebastian Jäger, Felix Bießmann

Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

We implement models that reliably predict product attributes across online shops, languages, or both, and can be used to match product taxonomies between online retailers.

Alexander Flick, Sebastian Jäger, Ivana Trajanovska, Felix Bießmann

GreenDB - A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods

We present a second public release of the GreenDB and present a first benchmark for sustainability information extraction.

Sebastian Jäger, Alexander Flick, Jessica Adriana Sanchez Garcia, Kaspar von den Driesch, Karl Brendel, Felix Bießmann

Nudging Sustainable Consumption: A Large-Scale Data Analysis of Sustainability Labels for Fashion in German Online Retail

A Large-Scale data analysis of sustainability labels for fashion in German online retail.

Maike Gossen, Sebastian Jäger, Marja Lena Hoffmann, Felix Bießmann, Ruben Korenke, Tilman Santarius

GreenDB: Toward a Product-by-Product Sustainability Database

We present the first public release of the GreenDB and describe its scraping pipeline.

Sebastian Jäger, Jessica Greene, Max Jakob, Ruben Korenke, Tilman Santarius, Felix Bießmann

A Benchmark for Data Imputation Methods

Comparison of data imputation methods on a wide range of datasets, missingness patterns, and missingness fractions.

Sebastian Jäger, Arndt Allhorn, Felix Bießmann

Compressing BERT - An Evaluation and Combination of Methods

Evaluation and improvement of BERT-of-Theseus based on real world datasets and tasks.

Sebastian Jäger

Parallelized Training of Deep NN – Comparison of Current Concepts and Frameworks

Kubernetes based evaluation of TensorFlows’ and MXNet’s throughput, scalability and practical ease of use.

Sebastian Jäger, Hans Peter Zorn, Stefan Igel, Christian Zirpins

Machine Learning Im Kubernetes Cluster

Erläutert wie Kubernetes und das passenden Tooling hilft, Machine-Learning-Projekte effizient und erfolgreich umzusetzen.

Sebastian Jäger

Horizontales Skalieren Von Deep Learning Frameworks

Kubernetes basierte Evaluation der horizontalen Skalierbarkeit von TensorFlow und MXNet mit Hilfe der Datensätze Fashion-MNIST und PTB.

Sebastian Jäger