5 citations · 14 across the 7 of their papers we have counts for
9 papers
Few-shot Learning for Unsupervised Feature Selection
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually requir…
Meta-Learning for Relative Density-Ratio Estimation
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of th…
Fast Subgraph Matching by Exploiting Search Failures
Junya Arai, Makoto Onizuka, Yasuhiro Fujiwara +1
Subgraph matching is a compute-intensive problem that asks to enumerate all the isomorphic embeddings of a query graph within a data graph. This problem is generally solved with ba…
Sequenced Route Query with Semantic Hierarchy
Yuya Sasaki, Yoshiharu Ishikawa, Yasuhiro Fujiwara +1
The trip planning query searches for preferred routes starting from a given point through multiple Point-of-Interests (PoI) that match user requirements. Although previous studies…
Efficient Network Reliability Computation in Uncertain Graphs
Yuya Sasaki, Yasuhiro Fujiwara, Makoto Onizuka
Network reliability is an important metric to evaluate the connectivity among given vertices in uncertain graphs. Since the network reliability problem is known as #P-complete, exi…
Semi-supervised Anomaly Detection on Attributed Graphs
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anoma…