output
20022025
most citedLearning Convolutional Neural Networks for Graphs

808 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020★ 13 cited

Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications

Shujian Yu, Ammar Shaker, Francesco Alesiani +1

We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions. The new statistic avoids the explicit estimation of the underlyin…

cs.LG2020

HAMLET -- A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection

Mischa Schmidt, Julia Gastinger, Sébastien Nicolas +1

Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning. Traditional multi-armed bandit strategies look to the history of observed r…

cs.LG2018★ 4 cited

Efficient and Scalable Multi-task Regression on Massive Number of Tasks

Xiao He, Francesco Alesiani, Ammar Shaker

Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. How…

cs.LG2016★ 808 cited

Learning Convolutional Neural Networks for Graphs

Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov

Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be u…