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Satoshi Oyama

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12 papers

2 author rows

AAAI Conference 2026 Short Paper

Behavioral-Similarity and Clustering-Based Methods for Static Graph Estimation in Hybrid GNNs (Student Abstract)

Ryusei Otani
Keichi Namikoshi
Yuko Sakurai
Mingyu Guo
Satoshi Oyama

In this study, we propose two methods to estimate static graphs from a single dynamic graph and integrate them into hybrid Graph Neural Networks (GNNs), which combine long-term static structure with transient dynamic interactions. Since static graphs are often unavailable and attributes may be difficult to use at scale or under privacy constraints, we introduce: (i) a “behavioral similarity” estimator based on normalized co-occurrence, which requires no attributes, and (ii) an attribute-aware K-means + k-NN estimator that is more efficient than cosine similarity. Experiments on multiple real-world datasets show that both methods consistently improve predictive accuracy and training efficiency, underscoring the importance of static graph choice in hybrid GNNs.

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AAMAS Conference 2026 Conference Paper

MACC: Multi-Agent Collaborative Competition for Scientific Exploration

Satoshi Oyama
Yuko Sakurai
Hisashi Kashima

Scientific discovery still relies heavily on the manual efforts of individual researchers, leading to limited exploration, redundant trials, and reduced reproducibility. Human-participant data analysis competitions generate diverse approaches, yet fluctuations in participation and the lack of independent repetitions show that parallel exploration alone is insufficient for achieving reliable scientific inquiry. As advanced AI agents based on large language models (LLMs) increasingly perform analytical tasks, relying on a single highly capable agent is unlikely to overcome these structural limitations. Recent work has begun to explore how multiple LLMbased agents can collaborate or compete in scientific workflows—a growing trend we refer to as MA4Science. However, most existing MA4Science studies assume that all agents are controlled by a single organizational entity, limiting their ability to examine how institutional mechanisms—such as incentives, information sharing, and reproducibility—shape collective exploration among independently managed agents. To address this gap, we introduce MACC (Multi-Agent Collaborative Competition), an institutional architecture that integrates a blackboard-style shared scientific workspace with incentive mechanisms designed to encourage transparency, reproducibility, and exploration efficiency. MACC provides a testbed for studying how institutional design influences scalable and reliable multi-agent scientific exploration.

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AAAI Conference 2025 Short Paper

Counterfactual Explanations of Time Varying Rankings (Student Abstract)

Ryusei Ohtani
Yuko Sakurai
Satoshi Oyama

Counterfactual explanations in Explainable AI (XAI) identify which features to change to alter an outcome, but existing methods adjust only the features of a single agent. We present a new approach to re-evaluating rankings that is based on predictions of future features of the other agents in a ranking system. It uses an algorithm that provides a more realistic counterfactual explanation of changing the ranking of a particular agent. Computer experiments demonstrated that the proposed algorithm can capture the time variation of the entire ranking system in the inference results.

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JMLR Journal 2021 Journal Article

Factorization Machines with Regularization for Sparse Feature Interactions

Kyohei Atarashi
Satoshi Oyama
Masahito Kurihara

Factorization machines (FMs) are machine learning predictive models based on second-order feature interactions and FMs with sparse regularization are called sparse FMs. Such regularizations enable feature selection, which selects the most relevant features for accurate prediction, and therefore they can contribute to the improvement of the model accuracy and interpretability. However, because FMs use second-order feature interactions, the selection of features often causes the loss of many relevant feature interactions in the resultant models. In such cases, FMs with regularization specially designed for feature interaction selection trying to achieve interaction-level sparsity may be preferred instead of those just for feature selection trying to achieve feature-level sparsity. In this paper, we present a new regularization scheme for feature interaction selection in FMs. For feature interaction selection, our proposed regularizer makes the feature interaction matrix sparse without a restriction on sparsity patterns imposed by the existing methods. We also describe efficient proximal algorithms for the proposed FMs and how our ideas can be applied or extended to feature selection and other related models such as higher-order FMs and the all-subsets model. The analysis and experimental results on synthetic and real-world datasets show the effectiveness of the proposed methods. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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IJCAI Conference 2020 Conference Paper

Generating Natural Counterfactual Visual Explanations

Wenqi Zhao
Satoshi Oyama
Masahito Kurihara

Counterfactual explanations help users to understand the behaviors of machine learning models by changing the inputs for the existing outputs. For an image classification task, an example counterfactual visual explanation explains: "for an example that belongs to class A, what changes do we need to make to the input so that the output is more inclined to class B. " Our research considers changing the attribute description text of class A on the basis of the attributes of class B and generating counterfactual images on the basis of the modified text. We can use the prediction results of the model on counterfactual images to find the attributes that have the greatest effect when the model is predicting classes A and B. We applied our method to a fine-grained image classification dataset and used the generative adversarial network to generate natural counterfactual visual explanations. To evaluate these explanations, we used them to assist crowdsourcing workers in an image classification task. We found that, within a specific range, they improved classification accuracy.

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AAAI Conference 2019 Conference Paper

Random Feature Maps for the Itemset Kernel

Kyohei Atarashi
Subhransu Maji
Satoshi Oyama

Although kernel methods efficiently use feature combinations without computing them directly, they do not scale well with the size of the training dataset. Factorization machines (FMs) and related models, on the other hand, enable feature combinations efficiently, but their optimization generally requires solving a non-convex problem. We present random feature maps for the itemset kernel, which uses feature combinations, and includes the ANOVA kernel, the all-subsets kernel, and the standard dot product. Linear models using one of our proposed maps can be used as an alternative to kernel methods and FMs, resulting in better scalability during both training and evaluation. We also present theoretical results for a proposed map, discuss the relationship between factorization machines and linear models using a proposed map for the ANOVA kernel, and relate the proposed feature maps to prior work. Furthermore, we show that the maps can be calculated more efficiently by using a signed circulant matrix projection technique. Finally, we demonstrate the effectiveness of using the proposed maps for real-world datasets.

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AAAI Conference 2018 Conference Paper

Data Analysis Competition Platform for Educational Purposes: Lessons Learned and Future Challenges

Yukino Baba
Tomoumi Takase
Kyohei Atarashi
Satoshi Oyama
Hisashi Kashima

Data analysis education plays an important role in accelerating the efﬁcient use of data analysis technologies in various domains. Not only the knowledge of statistics and machine learning, but also practical skills of deploying machine learning and data analysis techniques, are required for conducting data analysis projects in the real world. Data analysis competitions, such as Kaggle, have been considered as an efﬁcient system for learning such skills by addressing real data analysis problems. However, current data analysis competitions are not designed for educational purposes and it is not well studied how data analysis competition platforms should be designed for enhancing educational effectiveness. To answer this research question, we built, and subsequently operated an educational data analysis competition platform called University of Big Data for several years. In this paper, we present our approaches for supporting and motivating learners and the results of our case studies. We found that providing a tutorial article is beneﬁcial for encouraging active participation of learners, and a leaderboard system allowing an unlimited number of submissions can motivate the efforts of learners. We further discuss future directions of educational data analysis competitions.

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AAAI Conference 2018 Conference Paper

Semi-Supervised Learning From Crowds Using Deep Generative Models

Kyohei Atarashi
Satoshi Oyama
Masahito Kurihara

Although supervised learning requires a labeled dataset, obtaining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the ﬁeld of machine learning as a way to collect labels at relatively low cost. However, the labels obtained by crowdsourcing, i. e. , from non-expert workers, are often noisy. A number of methods have thus been devised for inferring true labels, and several methods have been proposed for learning classiﬁers directly from crowdsourced labels, referred to as learning from crowds. A more practical problem is learning from crowdsourced labeled data and unlabeled data, i. e. , semi-supervised learning from crowds. This paper presents a novel generative model of the labeling process in crowdsourcing. It leverages unlabeled data effectively by introducing latent features and a data distribution. Because the data distribution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can be regarded as a kind of deep generative model. The problems caused by the intractability of latent variable posteriors is solved by introducing an inference model. The experiments show that it outperforms four existing models, including a baseline model, on the MNIST dataset with simulated workers and the Rotten Tomatoes movie review dataset with Amazon Mechanical Turk workers.

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IJCAI Conference 2015 Conference Paper

Crowdsourced Semantic Matching of Multi-Label Annotations

Lei Duan
Satoshi Oyama
Masahito Kurihara
Haruhiko Sato

Most multi-label domains lack an authoritative taxonomy. Therefore, different taxonomies are commonly used in the same domain, which results in complications. Although this situation occurs frequently, there has been little study of it using a principled statistical approach. Given that (1) different taxonomies used in the same domain are generally founded on the same latent semantic space, where each possible label set in a taxonomy denotes a single semantic concept, and that (2) crowdsourcing is beneficial in identifying relationships between semantic concepts and instances at low cost, we proposed a novel probabilistic cascaded method for establishing a semantic matching function in a crowdsourcing setting that maps label sets in one (source) taxonomy to label sets in another (target) taxonomy in terms of the semantic distances between them. The established function can be used to detect the associated label set in the target taxonomy for an instance directly from its associated label set in the source taxonomy without any extra effort. Experimental results on real-world data (emotion annotations for narrative sentences) demonstrated that the proposed method can robustly establish semantic matching functions exhibiting satisfactory performance from a limited number of crowdsourced annotations.

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IJCAI Conference 2013 Conference Paper

Accurate Integration of Crowdsourced Labels Using Workers' Self-Reported Confidence Scores

Satoshi Oyama
Yukino Baba
Yuko Sakurai
Hisashi Kashima

We have developed a method for using confidence scores to integrate labels provided by crowdsourcing workers. Although confidence scores can be useful information for estimating the quality of the provided labels, a way to effectively incorporate them into the integration process has not been established. Moreover, some workers are overconfident about the quality of their labels while others are underconfident, and some workers are quite accurate in judging the quality of their labels. This differing reliability of the confidence scores among workers means that the probability distributions for the reported confidence scores differ among workers. To address this problem, we extended the Dawid-Skene model and created two probabilistic models in which the values of unobserved true labels are inferred from the observed provided labels and reported confidence scores by using the expectation-maximization algorithm. Results of experiments using actual crowdsourced data for image labeling and binary question answering tasks showed that incorporating workers’ confidence scores can improve the accuracy of integrated crowdsourced labels.

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JMLR Journal 2011 Journal Article

Learning a Robust Relevance Model for Search Using Kernel Methods

Wei Wu
Jun Xu
Hang Li
Satoshi Oyama

This paper points out that many search relevance models in information retrieval, such as the Vector Space Model, BM25 and Language Models for Information Retrieval, can be viewed as a similarity function between pairs of objects of different types, referred to as an S-function. An S-function is specifically defined as the dot product between the images of two objects in a Hilbert space mapped from two different input spaces. One advantage of taking this view is that one can take a unified and principled approach to address the issues with regard to search relevance. The paper then proposes employing a kernel method to learn a robust relevance model as an S-function, which can effectively deal with the term mismatch problem, one of the biggest challenges in search. The kernel method exploits a positive semi-definite kernel referred to as an S-kernel. The paper shows that when using an S-kernel the model learned by the kernel method is guaranteed to be an S-function. The paper then gives more general principles for constructing S-kernels. A specific implementation of the kernel method is proposed using the Ranking SVM techniques and click-through data. The proposed approach is employed to learn a relevance model as an extension of BM25, referred to as Robust BM25. Experimental results on web search and enterprise search data show that Robust BM25 significantly outperforms baseline methods and can successfully tackle the term mismatch problem. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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ECAI Conference 2008 Conference Paper

How Many Objects? : Determining the Number of Clusters with a Skewed Distribution

Satoshi Oyama
Katsumi Tanaka

We propose a supervised approach to enable accurate determination of the number of clusters in object identification. We use the aggregated attribute values of the data set to be clustered as explanatory variables in the prediction model. Attribute aggregation can be done in linear time with respect to the number of data items, so our method can be used to predict the number of clusters with a low computational burden. To deal with skewed target values, we introduce a two-stage method as well as a method using a higher-order combination of explanatory variables. Experiments demonstrate our methods enable more accurate prediction than existing methods.

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