no code implementations • ICML 2020 • Yasutoshi Ida, Sekitoshi Kanai, Yasuhiro Fujiwara, Tomoharu Iwata, Koh Takeuchi, Hisashi Kashima
This is because coordinate descent iteratively updates all the parameters in the objective until convergence.
1 code implementation • 25 Sep 2023 • Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu, Han Bao, Koh Takeuchi, Hisashi Kashima
One popular application of this estimation lies in the prediction of the impact of a treatment (e. g., a promotion) on an outcome (e. g., sales) of a particular unit (e. g., an item), known as the individual treatment effect (ITE).
1 code implementation • The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023 • Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi
To address this problem, we propose a spatial intervention neural network (SINet) that leverages the hierarchical structure of spatial graphs to learn a rich representation of the covariates and the treatments and exploits this representation to predict a time series of treatment outcome.
no code implementations • 20 Jul 2023 • Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima
The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data.
no code implementations • 25 Feb 2023 • Ryosuke Ueda, Koh Takeuchi, Hisashi Kashima
Crowdsourcing has been widely used to efficiently obtain labeled datasets for supervised learning from large numbers of human resources at low cost.
1 code implementation • 8 Feb 2023 • Xiaotian Lu, Jiyi Li, Koh Takeuchi, Hisashi Kashima
Crowdsourcing has been used to collect data at scale in numerous fields.
no code implementations • 4 Jun 2022 • Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
Evaluation of intervention in a multiagent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
1 code implementation • 15 Dec 2021 • Sein Minn, Jill-Jenn Vie, Koh Takeuchi, Hisashi Kashima, Feida Zhu
IKT's prediction of future student performance is made using a Tree-Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning-based student models.
no code implementations • 27 Jun 2021 • Xiaotian Lu, Arseny Tolmachev, Tatsuya Yamamoto, Koh Takeuchi, Seiji Okajima, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima
In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans.
1 code implementation • 11 Jun 2021 • Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities.
no code implementations • 19 Feb 2021 • Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
1 code implementation • 8 Feb 2021 • Koh Takeuchi, Ryo Nishida, Hisashi Kashima, Masaki Onishi
In this paper, we consider the problem of estimating the effects of crowd movement guidance from past data.
1 code implementation • 21 May 2020 • Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei
To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.
no code implementations • 8 Feb 2018 • Akisato Kimura, Zoubin Ghahramani, Koh Takeuchi, Tomoharu Iwata, Naonori Ueda
In this paper, we propose a simple but effective method for training neural networks with a limited amount of training data.
no code implementations • 22 Mar 2016 • Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski
We introduce the localized Lasso, which is suited for learning models that are both interpretable and have a high predictive power in problems with high dimensionality $d$ and small sample size $n$.