92 citations · 178 across the 10 of their papers we have counts for
12 papers
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
Dai Hai Nguyen, Tetsuya Sakurai, Hiroshi Mamitsuka
Variational inference (VI) can be cast as an optimization problem in which the variational parameters are tuned to closely align a variational distribution with the true posterior.…
Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions
Duc Anh Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Predicting drug-drug interactions (DDI) is the problem of predicting side effects (unwanted outcomes) of a pair of drugs using drug information and known side effects of many pairs…
Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels
Dai Hai Nguyen, Canh Hao Nguyen, Hiroshi Mamitsuka
Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks. A popular approach to learningwith graph…
On Convex Clustering Solutions
Canh Hao Nguyen, Hiroshi Mamitsuka
Convex clustering is an attractive clustering algorithm with favorable properties such as efficiency and optimality owing to its convex formulation. It is thought to generalize bot…
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
Betül Güvenç Paltun, Samuel Kaski, Hiroshi Mamitsuka
Detecting predictive biomarkers from multi-omics data is important for precision medicine, to improve diagnostics of complex diseases and for better treatments. This needs substant…
Scalable Probabilistic Matrix Factorization with Graph-Based Priors
Jonathan Strahl, Jaakko Peltonen, Hiroshi Mamitsuka +1
In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations lea…
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Ronghui You, Zihan Zhang, Ziye Wang +3
Extreme multi-label text classification (XMTC) is an important problem in the era of big data, for tagging a given text with the most relevant multiple labels from an extremely lar…
Learning on Hypergraphs with Sparsity
Canh Hao Nguyen, Hiroshi Mamitsuka
Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds…
Convex Coupled Matrix and Tensor Completion
Kishan Wimalawarne, Makoto Yamada, Hiroshi Mamitsuka
We propose a set of convex low rank inducing norms for a coupled matrices and tensors (hereafter coupled tensors), which shares information between matrices and tensors through com…
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
Sparse Learning over Infinite Subgraph Features
Ichigaku Takigawa, Hiroshi Mamitsuka
We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph f…
Gene-Proximity Models For Genome-Wide Association Studies
Ian Johnston, Timothy Hancock, Hiroshi Mamitsuka +1
Motivated by the important problem of detecting association between genetic markers and binary traits in genome-wide association studies, we present a novel Bayesian model that est…