Machine Learning
For a full list of our machine learning projects and publications please see the list of research articles of the lab.
Below, you can find additional information (code, data, supplement) on some of our Machine Learning projects:
Similarity Measures for Structured Data
We develop methods for comparing and classifying structured objects such as graphs, strings or time series. One prominent example are graph kernels, i.e. efficient distance functions between graphs.
High-Dimensional Data Analysis
We develop methods for measuring statistical dependence between high dimensional variables, two-sample tests to tell whether two samples were drawn from the same distribution, outlier detection algorithms to tell find "unusual" observations in a given dataset, clustering algorithms that integrate different views of the data, and approaches that detect non-linear dependence between variables.
- chevron_right Kernel Method for the Two Sample Problem (MMD)
- chevron_right Non-linear measures of statistical dependence (MID)
- chevron_right Rapid Outlier Detection via Sampling
- chevron_right A Confounder-Corrected Support Vector Machine Classifier (ccSVM)
- chevron_right Multi-view Spectral Clustering on Conflicting Views
- chevron_right Kernel Conditional Clustering
Significant Pattern Mining
We develop methods that discover significant patterns in high dimensional datasets while being runtime efficient and statistically sound. Our algorithms can be applied to graphs or collections of sequences and allow to account for dependencies between objects, to control the Family-Wise Error Rate and to correct for categorical covariates.