Jilles Vreeken

research

∙ 07/14/2023

Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent

Addressing the interpretability problem of NMF on Boolean data, Boolean ...

0 Sebastian Dalleiger, et al. ∙

research

∙ 01/31/2023

Preserving local densities in low-dimensional embeddings

Low-dimensional embeddings and visualizations are an indispensable tool ...

0 Jonas Fischer, et al. ∙

research

∙ 06/16/2022

All the World's a (Hyper)Graph: A Data Drama

We introduce Hyperbard, a dataset of diverse relational data representat...

74 Corinna Coupette, et al. ∙

research

∙ 12/16/2021

Differentially Describing Groups of Graphs

How does neural connectivity in autistic children differ from neural con...

0 Corinna Coupette, et al. ∙

research

∙ 10/18/2021

Label-Descriptive Patterns and their Application to Characterizing Classification Errors

State-of-the-art deep learning methods achieve human-like performance on...

0 Michael Hedderich, et al. ∙

research

∙ 05/29/2021

Graph Similarity Description: How Are These Graphs Similar?

How do social networks differ across platforms? How do information netwo...

0 Corinna Coupette, et al. ∙

research

∙ 05/05/2021

Formally Justifying MDL-based Inference of Cause and Effect

The algorithmic independence of conditionals, which postulates that the ...

0 Alexander Marx, et al. ∙

research

∙ 03/02/2021

Factoring out prior knowledge from low-dimensional embeddings

Low-dimensional embedding techniques such as tSNE and UMAP allow visuali...

0 Edith Heiter, et al. ∙

research

∙ 09/06/2020

Discovering Reliable Causal Rules

We study the problem of deriving policies, or rules, that when enacted o...

5 Kailash Budhathoki, et al. ∙

research

∙ 03/23/2020

What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

Knowledge graphs (KGs) store highly heterogeneous information about the ...

13 Caleb Belth, et al. ∙

research

∙ 08/30/2019

Discovering Reliable Correlations in Categorical Data

In many scientific tasks we are interested in discovering whether there ...

20 Panagiotis Mandros, et al. ∙

research

∙ 04/25/2019

Summarizing Data Succinctly with the Most Informative Itemsets

Knowledge discovery from data is an inherently iterative process. That i...

0 Michael Mampaey, et al. ∙

research

∙ 04/25/2019

Tell Me What I Need to Know: Succinctly Summarizing Data with Itemsets

Data analysis is an inherently iterative process. That is, what we know ...

0 Michael Mampaey, et al. ∙

research

∙ 03/12/2019

Testing Conditional Independence on Discrete Data using Stochastic Complexity

Testing for conditional independence is a core aspect of constraint-base...

8 Alexander Marx, et al. ∙

research

∙ 02/18/2019

Comparing Apples and Oranges: Measuring Differences between Exploratory Data Mining Results

Deciding whether the results of two different mining algorithms provide ...

0 Nikolaj Tatti, et al. ∙

research

∙ 02/18/2019

Comparing Apples and Oranges: Measuring Differences between Data Mining Results

Deciding whether the results of two different mining algorithms provide ...

0 Nikolaj Tatti, et al. ∙

research

∙ 02/07/2019

Discovering Descriptive Tile Trees by Mining Optimal Geometric Subtiles

When analysing binary data, the ease at which one can interpret results ...

0 Nikolaj Tatti, et al. ∙

research

∙ 02/07/2019

The Long and the Short of It: Summarising Event Sequences with Serial Episodes

An ideal outcome of pattern mining is a small set of informative pattern...

6 Nikolaj Tatti, et al. ∙

research

∙ 02/06/2019

Finding Good Itemsets by Packing Data

The problem of selecting small groups of itemsets that represent the dat...

0 Nikolaj Tatti, et al. ∙

research

∙ 01/21/2019

We Are Not Your Real Parents: Telling Causal from Confounded using MDL

Given data over variables (X_1,...,X_m, Y) we consider the problem of fi...

14 David Kaltenpoth, et al. ∙

research

∙ 09/14/2018

Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms

The reliable fraction of information is an attractive score for quantify...

11 Panagiotis Mandros, et al. ∙

research

∙ 08/20/2018

Causal Discovery by Telling Apart Parents and Children

We consider the problem of inferring the directed, causal graph from obs...

4 Alexander Marx, et al. ∙

research

∙ 11/15/2017

CTRL+Z: Recovering Anonymized Social Graphs

Social graphs derived from online social interactions contain a wealth o...

0 Yang Zhang, et al. ∙

research

∙ 09/26/2017

Telling Cause from Effect using MDL-based Local and Global Regression

We consider the fundamental problem of inferring the causal direction be...

0 Alexander Marx, et al. ∙

research

∙ 09/22/2017

Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups

Subgroup discovery is a local pattern mining technique to find interpret...

0 Janis Kalofolias, et al. ∙

research

∙ 05/25/2017

Discovering Reliable Approximate Functional Dependencies

Given a database and a target attribute of interest, how can we tell whe...

1 Panagiotis Mandros, et al. ∙

research

∙ 02/22/2017

Causal Inference by Stochastic Complexity

The algorithmic Markov condition states that the most likely causal dire...

0 Kailash Budhathoki, et al. ∙

research

∙ 02/21/2017

Causal Inference on Multivariate and Mixed-Type Data

Given data over the joint distribution of two random variables X and Y, ...

0 Alexander Marx, et al. ∙

research

∙ 01/27/2017

Efficiently Summarising Event Sequences with Rich Interleaving Patterns

Discovering the key structure of a database is one of the main goals of ...

0 Apratim Bhattacharyya, et al. ∙

research

∙ 01/26/2017

Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery

Existing algorithms for subgroup discovery with numerical targets do not...

0 Mario Boley, et al. ∙

research

∙ 12/22/2015

Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns

We study how to obtain concise descriptions of discrete multivariate seq...

0 Roel Bertens, et al. ∙

research

∙ 12/22/2015

Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences

Our world is filled with both beautiful and brainy people, but how often...

0 Roel Bertens, et al. ∙

research

∙ 10/28/2015

Universal Dependency Analysis

Most data is multi-dimensional. Discovering whether any subset of dimens...

0 Hoang-Vu Nguyen, et al. ∙

research

∙ 10/28/2015

Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series

Change detection in multivariate time series has applications in many do...

0 Hoang-Vu Nguyen, et al. ∙

research

∙ 10/28/2015

Flexibly Mining Better Subgroups

In subgroup discovery, also known as supervised pattern mining, discover...

0 Hoang-Vu Nguyen, et al. ∙

research

∙ 10/28/2015

Canonical Divergence Analysis

We aim to analyze the relation between two random vectors that may poten...

0 Hoang-Vu Nguyen, et al. ∙

Jilles Vreeken

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