14 citations · 57 across the 17 of their papers we have counts for
17 papers
Feature Importance Measurement based on Decision Tree Sampling
Chao Huang, Diptesh Das, Koji Tsuda
Random forest is effective for prediction tasks but the randomness of tree generation hinders interpretability in feature importance analysis. To address this, we proposed DT-Sampl…
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning
Takumi Yoshida, Hiroyuki Hanada, Kazuya Nakagawa +3
Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea be…
On a linear fused Gromov-Wasserstein distance for graph structured data
Dai Hai Nguyen, Koji Tsuda
We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem. T…
Fast and More Powerful Selective Inference for Sparse High-order Interaction Model
Diptesh Das, Vo Nguyen Le Duy, Hiroyuki Hanada +2
Automated high-stake decision-making such as medical diagnosis requires models with high interpretability and reliability. As one of the interpretable and reliable models with good…
Transductive Boltzmann Machines
Mahito Sugiyama, Koji Tsuda, Hiroyuki Nakahara
We present transductive Boltzmann machines (TBMs), which firstly achieve transductive learning of the Gibbs distribution. While exact learning of the Gibbs distribution is impossib…
Legendre Decomposition for Tensors
Mahito Sugiyama, Hiroyuki Nakahara, Koji Tsuda
We present a novel nonnegative tensor decomposition method, called Legendre decomposition, which factorizes an input tensor into a multiplicative combination of parameters. Thanks…