Titlebook: Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track; European Conference, Albert Bifet,Povilas Daniušis,In

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Real-Part Quantum Support Vector Machinesmental discipline. Within quantum computing, quantum machine learning is becoming more and more popular. However, subtle differences between classical and quantum machine learning methods sometimes lead to incompatible formalizations of otherwise well aligned methods. Inspired by this observation, w

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Enhancing Shortest-Path Graph Kernels via Graph Augmentationt compare graphs with variable scales of the graph structure well and cannot compare attributed graphs whose vertices have continuous attributes. To overcome these two challenges, we propose to enhance SP via graph augmentation: Variable scales of the graph structure around vertices are extracted to

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Hyperbolic Contrastive Learning with Model-Augmentation for Knowledge-Aware Recommendationnowledge-aware recommendation. However, most existing contrastive learning-based methods have difficulties in effectively capturing the underlying hierarchical structure within user-item bipartite graphs and knowledge graphs. Moreover, they commonly generate positive samples for contrastive learning

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Adaptive Knowledge Distillation for Classification of Hand Images Using Explainable Vision Transformnct characteristics, such as the pattern of veins, fingerprints, and the geometry of the hand itself. This paper investigates the use of vision transformers (ViTs) for classification of hand images. We use explainability tools to explore the internal representations of ViTs and assess their impact o

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Lifelong Hierarchical Topic Modeling via Nonparametric Word Embedding Clusteringwever, the existing methods often assume a fixed topic hierarchy, leading to poor performance when applied to document streams. Meanwhile, the prior knowledge of topic structure is helpful for hierarchical topic modeling but it is quite costly to obtain such information manually. To address these is

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High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspacesay decrease exponentially as the dimensionality increases. A common framework to tackle this problem is to assume that the objective function depends on a limited set of features that lie on a low-dimensional manifold embedded in the high-dimensional ambient space. The latent space can be linear or