Search Results for author: Emmanuel Müller

Found 13 papers, 10 papers with code

Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

1 code implementation17 May 2022 Chiara Balestra, Florian Huber, Andreas Mayr, Emmanuel Müller

Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.

Anomaly Detection Feature Importance +1

Statistical Evaluation of Anomaly Detectors for Sequences

1 code implementation13 Aug 2020 Erik Scharwächter, Emmanuel Müller

Although precision and recall are standard performance measures for anomaly detection, their statistical properties in sequential detection settings are poorly understood.

Anomaly Detection

Graph Clustering with Graph Neural Networks

no code implementations30 Jun 2020 Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.

Graph Clustering Link Prediction +1

Differentiable Segmentation of Sequences

1 code implementation ICLR 2021 Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller

We build on recent advances in learning continuous warping functions and propose a novel family of warping functions based on the two-sided power (TSP) distribution.

Change Point Detection

FREDE: Linear-Space Anytime Graph Embeddings

no code implementations8 Jun 2020 Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan Oseledets, Emmanuel Müller

Low-dimensional representations, or embeddings, of a graph's nodes facilitate data mining tasks.

Does Terrorism Trigger Online Hate Speech? On the Association of Events and Time Series

1 code implementation30 Apr 2020 Erik Scharwächter, Emmanuel Müller

We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series.

Causal Inference Point Processes +1

Two-Sample Testing for Event Impacts in Time Series

1 code implementation31 Jan 2020 Erik Scharwächter, Emmanuel Müller

Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest.

Event Detection Time Series +1

The Shape of Data: Intrinsic Distance for Data Distributions

2 code implementations ICLR 2020 Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, Emmanuel Müller

The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures.

SGR: Self-Supervised Spectral Graph Representation Learning

no code implementations15 Nov 2018 Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller

Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand.

Graph Representation Learning

Deep One-Class Classification

1 code implementation ICML 2018 Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.

Anomaly Detection Classification +2

NetLSD: Hearing the Shape of a Graph

1 code implementation27 May 2018 Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller

However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation.

Social and Information Networks

VERSE: Versatile Graph Embeddings from Similarity Measures

2 code implementations13 Mar 2018 Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller

Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization.

Link Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.