402 citations · 913 across the 36 of their papers we have counts for
10 papers · 1 filter
Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent
Sebastian Dalleiger, Jilles Vreeken
Addressing the interpretability problem of NMF on Boolean data, Boolean Matrix Factorization (BMF) uses Boolean algebra to decompose the input into low-rank Boolean factor matrices…
Preserving local densities in low-dimensional embeddings
Jonas Fischer, Rebekka Burkholz, Jilles Vreeken
Low-dimensional embeddings and visualizations are an indispensable tool for analysis of high-dimensional data. State-of-the-art methods, such as tSNE and UMAP, excel in unveiling l…
All the World's a (Hyper)Graph: A Data Drama
Corinna Coupette, Jilles Vreeken, Bastian Rieck
We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare's plays. Our representations range from simple graphs capturing character co-o…
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives i…
Federated Learning from Small Datasets
Michael Kamp, Jonas Fischer, Jilles Vreeken
Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherentl…
Factoring out prior knowledge from low-dimensional embeddings
Edith Heiter, Jonas Fischer, Jilles Vreeken
Low-dimensional embedding techniques such as tSNE and UMAP allow visualizing high-dimensional data and therewith facilitate the discovery of interesting structure. Although they ar…
Discovering Reliable Causal Rules
Kailash Budhathoki, Mario Boley, Jilles Vreeken
We study the problem of deriving policies, or rules, that when enacted on a complex system, cause a desired outcome. Absent the ability to perform controlled experiments, such rule…
Discovering Reliable Correlations in Categorical Data
Panagiotis Mandros, Mario Boley, Jilles Vreeken
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably me…
We Are Not Your Real Parents: Telling Causal from Confounded using MDL
David Kaltenpoth, Jilles Vreeken
Given data over variables we consider the problem of finding out whether jointly causes or whether they are all confounded by an unobserved latent variab…
Causal Inference by Stochastic Complexity
Kailash Budhathoki, Jilles Vreeken
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov co…