👋 This is the GitHub page of the research group Methods for Big Data at the Scientific Computing Center, Karlsruhe Institute of Technology (KIT).
📚 Here, you can find repositories of our research projects at the intersection of machine learning and traditional statistical methods, with a focus on Bayesian methods.
🖥️ For more information regarding our group, feel free to visit our website.
Material for the DagStat Tutorial "Distributional Regression – Models and Applications" by Nadja Klein and Lucas Kock
Forked from mkrtl/BoostingDAGs
This repository contains the code for using boosting for causal discovery. The procedures are described at: https://arxiv.org/abs/2401.06523
Python
Forked from kocklucx/multGAMLSS
Code for the paper "Truly Multivariate Structured Additive Distributional Regression" by Lucas Kock and Nadja Klein
Python
Implementation of several research papers focused on uncertainty estimation and auto-labeling within the context of object detection for autonomous driving applications. It utilizes the Keras implementation of EfficientDet as an exemplary detector for demonstration purposes.
Material for the DagStat Tutorial "Distributional Regression – Models and Applications" by Nadja Klein and Lucas Kock
Official implementation of the WACV 2025 paper WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields.
Code for the paper "Truly Multivariate Structured Additive Distributional Regression" by Lucas Kock and Nadja Klein
Code for the DMLMM as introduced in the paper "Deep mixture of linear mixed models for complex longitudinal data" by Lucas Kock, Nadja Klein and David J. Nott.
This repository contains the code for using boosting for causal discovery. The procedures are described at: https://arxiv.org/abs/2401.06523
This repository hosts the R code used to produce the simulations and application results of the manuscript titled "Boosting Distributional Copula Regression for Bivariate Binary, Discrete and Mixed Responses"
R and C++ code for performing posterior inference for Bayesian Conditional Transformation models illustrated for three different applications.
