623 citations · 931 across the 9 of their papers we have counts for
11 papers
Add and Thin: Diffusion for Temporal Point Processes
David Lüdke, Marin Biloš, Oleksandr Shchur +2
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data. Even though these models can expr…
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
Oleksandr Shchur, Caner Turkmen, Nick Erickson +4
We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables use…
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +2
Automatically detecting anomalies in event data can provide substantial value in domains such as healthcare, DevOps, and information security. In this paper, we frame the problem o…
Neural Temporal Point Processes: A Review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +1
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with…
Fast and Flexible Temporal Point Processes with Triangular Maps
Oleksandr Shchur, Nicholas Gao, Marin Biloš +1
Temporal point process (TPP) models combined with recurrent neural networks provide a powerful framework for modeling continuous-time event data. While such models are flexible, th…
Overlapping Community Detection with Graph Neural Networks
Oleksandr Shchur, Stephan Günnemann
Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community det…
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating t…
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski +1
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on…
Dual-Primal Graph Convolutional Networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski +3
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Grap…
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner +1
We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased…
Introduction to Tensor Decompositions and their Applications in Machine Learning
Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann
Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions. While tensors first emerged in the psychometrics community in the…