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Koh Takeuchi

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6 papers

1 author row

AAMAS Conference 2026 Conference Paper

Mechanism-Informed Learning for Fair Division

Ryota Maruo
Tomohiko Yokoyama
Ayumi Igarashi
Koh Takeuchi

Fair division provides a simple yet powerful framework for modeling fairness in resource allocation. While existing literature typically assumes complete information about preferences, many practical scenarios involve incomplete preferences, posing challenges in computing fair allocations. In this paper, we propose mechanisminformed preference learning, a framework that integrates neural networks with differentiable approximations of classical fair division mechanisms—adjusted winner, round-robin, and movingknife—to estimate fair allocations from incomplete preferences. Experiments on real-world household chore preference data show that our mechanism-informed framework achieves fairer allocations, compared to methods without mechanism information.

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AAMAS Conference 2025 Conference Paper

Learning Fair and Preferable Allocations through Neural Network

Ryota Maruo
Koh Takeuchi
Hisashi Kashima

The fair allocation of indivisible resources is a fundamental problem. Existing research has developed various allocation mechanisms or algorithms to satisfy different fairness notions. For example, round robin (RR) was proposed to meet the fairness criterion known as envy-freeness up to one good (EF1). Expert algorithms without mathematical formulations are used in real-world resource allocation problems to find preferable outcomes for users. Therefore, we aim to design mechanisms that strictly satisfy good properties with replicating expert knowledge. However, this problem is challenging because such heuristic rules are often difficult to formalize mathematically, complicating their integration into theoretical frameworks. Additionally, formal algorithms struggle to find preferable outcomes, and directly replicating these implicit rules can result in unfair allocations because human decision-making can introduce biases. In this paper, we aim to learn implicit allocation mechanisms from examples while strictly satisfying fairness constraints, specifically focusing on learning EF1 allocation mechanisms through supervised learning on examples of reported valuations and corresponding allocation outcomes produced by implicit rules. To address this, we developed a neural RR (NRR), a novel neural network that parameterizes RR. NRR is built from a differentiable relaxation of RR and can be trained to learn the agent ordering used for RR. We conducted experiments to learn EF1 allocation mechanisms from examples, demonstrating that our method outperforms baselines in terms of the proximity of predicted allocations and other metrics.

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AAMAS Conference 2021 Conference Paper

Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference

Koh Takeuchi
Ryo Nishida
Hisashi Kashima
Masaki Onishi

Crowd movement guidance has been a fascinating problem in various fields, such as easing traffic congestion in unusual events and evacuating people from an emergency-affected area. To grab the reins of crowds, there has been considerable demand for a decision support system that can answer a typical question: “what will be the outcomes of each of the possible options in the current situation? ". In this paper, we consider the problem of estimating the effects of crowd movement guidance from past data. To cope with limited amount of available data biased by past decision-makers, we leverage two recent techniques in deep representation learning for spatial data analysis and causal inference. We use a spatial convolutional operator to extract effective spatial features of crowds from a small amount of data and use balanced representation learning based on the integral probability metrics to mitigate the selection bias and missing counterfactual outcomes. To evaluate the performance on estimating the treatment effects of possible guidance, we use a multi-agent simulator to generate realistic data on evacuation scenarios in a crowded theater, since there are no available datasets recording outcomes of all possible crowd movement guidance. The results of three experiments demonstrate that our proposed method reduces the estimation error by at most 56% from state-of-the-art methods.

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AAAI Conference 2018 Conference Paper

Mobile Network Failure Event Detection and Forecasting With Multiple User Activity Data Sets

Motoyuki Oki
Koh Takeuchi
Yukio Uematsu

As the demand for mobile network services increases, immediate detection and forecasting of network failure events have become important problems for service providers. Several event detection approaches have been proposed to tackle these problems by utilizing social data. However, these approaches have not tried to solve event detection and forecasting problems from multiple data sets, such as web access logs and search queries. In this paper, we propose a machine learning approach that incorporates multiple user activity data into detecting and forecasting failure events. Our approach is based on a two-level procedure. First, we introduce a novel feature construction method that treats both the imbalanced label problem and the data sparsity problem of user activity data. Second, we propose a model ensemble method that combines outputs of supervised and unsupervised learning models for each data set and gives accurate predictions of network service outage. We demonstrate the effectiveness of the proposed models by extensive experiments with realworld failure events occurred at a network service provider in Japan and three user activity data sets.

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IJCAI Conference 2017 Conference Paper

SVD-Based Screening for the Graphical Lasso

Yasuhiro Fujiwara
Naoki Marumo
Mathieu Blondel
Koh Takeuchi
Hideaki Kim
Tomoharu Iwata
Naonori Ueda

The graphical lasso is the most popular approach to estimating the inverse covariance matrix of high-dimension data. It iteratively estimates each row and column of the matrix in a round-robin style until convergence. However, the graphical lasso is infeasible due to its high computation cost for large size of datasets. This paper proposes Sting, a fast approach to the graphical lasso. In order to reduce the computation cost, it efficiently identifies blocks in the estimated matrix that have nonzero elements before entering the iterations by exploiting the singular value decomposition of data matrix. In addition, it selectively updates elements of the estimated matrix expected to have nonzero values. Theoretically, it guarantees to converge to the same result as the original algorithm of the graphical lasso. Experiments show that our approach is faster than existing approaches.

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IJCAI Conference 2013 Conference Paper

Non-Negative Multiple Matrix Factorization

Koh Takeuchi
Katsuhiko Ishiguro
Akisato Kimura
Hiroshi Sawada

Non-negative Matrix Factorization (NMF) is a traditional unsupervised machine learning technique for decomposing a matrix into a set of bases and coeﬃcients under the non-negative constraint. NMF with sparse constraints is also known for extracting reasonable components from noisy data. However, NMF tends to give undesired results in the case of highly sparse data, because the information included in the data is insuﬃcient to decompose. Our key idea is that we can ease this problem if complementary data are available that we could integrate into the estimation of the bases and coeﬃcients. In this paper, we propose a novel matrix factorization method called Non-negative Multiple Matrix Factorization (NMMF), which utilizes complementary data as auxiliary matrices that share the row or column indices of the target matrix. The data sparseness is improved by decomposing the target and auxiliary matrices simultaneously, since auxiliary matrices provide information about the bases and coeﬃcients. We formulate NMMF as a generalization of NMF, and then present a parameter estimation procedure derived from the multiplicative update rule. We examined NMMF in both synthetic and real data experiments. The eﬀect of the auxiliary matrices appeared in the improved NMMF performance. We also conﬁrmed that the bases that NMMF obtained from the real data were intuitive and reasonable thanks to the non-negative constraint.

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