250 citations · 937 across the 31 of their papers we have counts for
8 papers · 1 filter
Fisher Information Embedding for Node and Graph Learning
Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning no…
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Dexiong Chen, Bowen Fan, Carlos Oliver +1
We introduce Joint Multidimensional Scaling, a novel approach for unsupervised manifold alignment, which maps datasets from two different domains, without any known correspondences…
Structure-Aware Transformer for Graph Representation Learning
Dexiong Chen, Leslie O'Bray, Karsten Borgwardt
The Transformer architecture has gained growing attention in graph representation learning recently, as it naturally overcomes several limitations of graph neural networks (GNNs) b…
Finding Statistically Significant Interactions between Continuous Features
Mahito Sugiyama, Karsten Borgwardt
The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare…
Searching for significant patterns in stratified data
Felipe Llinares-Lopez, Laetitia Papaxanthos, Dean Bodenham +1
Significant pattern mining, the problem of finding itemsets that are significantly enriched in one class of objects, is statistically challenging, as the large space of candidate p…
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
Felipe Llinares López, Mahito Sugiyama, Laetitia Papaxanthos +1
We present a novel algorithm, Westfall-Young light, for detecting patterns, such as itemsets and subgraphs, which are statistically significantly enriched in one of two classes. Ou…