Deep convolutional networks have witnessed unprecedented success in various machine
learning applications. Formal understanding on what makes these networks so successful is …

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-
supervised learning problem on graphs. The proposed model shows extremely competitive …

Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

Active learning aims to reduce manual labeling efforts by proactively selecting the most
informative unlabeled instances to query. In real-world scenarios, it's often more practical to …

Active learning is a label-efficient approach to train highly effective models while
interactively selecting only small subsets of unlabelled data for labelling and training. In …

Message passing is the core of most graph models such as Graph Convolutional Network
(GCN) and Label Propagation (LP), which usually require a large number of clean labeled …

Graph sampling set selection, where a subset of nodes are chosen to collect samples to
reconstruct a smooth graph signal, is a fundamental problem in graph signal processing …

We study the problem of learning a binary classifier on the vertices of a graph. In particular,
we consider classifiers given by\emph {monophonic halfspaces}, partitions of the vertices …

We systematically study the query complexity of learning geodesically convex halfspaces on
graphs. Geodesic convexity is a natural generalisation of Euclidean convexity and allows …

Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency
of machine learning models by iteratively acquiring labels of data points with the highest …