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Hirofumi Suzuki

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables

Hirofumi Suzuki
Kentaro Kanamori
Takuya Takagi
Thong Pham
Takashi Nicholas Maeda
Shohei Shimizu

Causal discovery from observational data is a fundamental tool in various fields of science. While existing approaches are typically designed for a single dataset, we often need to handle multiple datasets with non-identical variable sets in practice. One straightforward approach is to estimate a causal graph from each dataset and construct a single causal graph by overlapping. However, this approach identifies limited causal relationships because unobserved variables in each dataset can be confounders, and some variable pairs may be unobserved in any dataset. To address this issue, we leverage Causal Additive Models with Unobserved Variables (CAM-UV) that provide causal graphs having information related to unobserved variables. We show that the ground truth causal graph has structural consistency with the information of CAM-UV on each dataset. As a result, we propose an approach named I-CAM-UV to integrate CAM-UV results by enumerating all consistent causal graphs. We also provide an efficient combinatorial search algorithm and demonstrate the usefulness of I-CAM-UV against existing methods.

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AAAI Conference 2026 Conference Paper

Sparse Additive Model Pruning for Order-Based Causal Structure Learning

Kentaro Kanamori
Hirofumi Suzuki
Takuya Takagi

Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a topological order of the underlying DAG and then prunes spurious edges from the fully-connected DAG induced by the estimated topological order. Previous studies often focus on the former ordering step because it can dramatically reduce the search space of DAGs. In practice, the latter pruning step is equally crucial for ensuring both computational efficiency and estimation accuracy. Most existing methods employ a pruning technique based on generalized additive models and hypothesis testing, commonly known as CAM-pruning. However, this approach can be a computational bottleneck as it requires repeatedly fitting additive models for all variables. Furthermore, it may harm estimation quality due to multiple testing. To address these issues, we introduce a new pruning method based on sparse additive models, which enables direct pruning of redundant edges without relying on hypothesis testing. We propose an efficient algorithm for learning sparse additive models by combining the randomized tree embedding technique with group-wise sparse regression. Experimental results on both synthetic and real datasets demonstrated that our method is significantly faster than existing pruning methods while maintaining comparable or superior accuracy.

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AAAI Conference 2022 Conference Paper

Explainable and Local Correction of Classification Models Using Decision Trees

Hirofumi Suzuki
Hiroaki Iwashita
Takuya Takagi
Keisuke Goto
Yuta Fujishige
Satoshi Hara

In practical machine learning, models are frequently updated, or corrected, to adapt to new datasets. In this study, we pose two challenges to model correction. First, the effects of corrections to the end-users need to be described explicitly, similar to standard software where the corrections are described as release notes. Second, the amount of corrections need to be small so that the corrected models perform similarly to the old models. In this study, we propose the first model correction method for classification models that resolves these two challenges. Our idea is to use an additional decision tree to correct the output of the old models. Thanks to the explainability of decision trees, the corrections are describable to the end-users, which resolves the first challenge. We resolve the second challenge by incorporating the amount of corrections when training the additional decision tree so that the effects of corrections to be small. Experiments on real data confirm the effectiveness of the proposed method compared to existing correction methods.

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ICRA Conference 2008 Conference Paper

A robotic finger equipped with an optical three-axis tactile sensor

Masahiro Ohka
Nobuyuki Morisawa
Hirofumi Suzuki
Jumpei Takata
Hiroaki Kobayashi
Hanafiah B. Yussof

In a previous paper we developed an optical three-axis tactile sensor that can acquire normal and shearing forces to be mounted on a robotic finger. Normal and shearing forces applied to the sensing element were detected separately; when we examined the repeatability of the present tactile sensor with 1, 000 loading-unloading cycles, the respective error of the normal forces was 2%. In the present paper, the three-axis tactile sensor is mounted on a robotic finger of three degrees of freedom to evaluate it for dexterous hands. A series of three kinds of experiments were performed. First, the robotic hand touches and scans flat specimens to evaluate the sensing ability of the friction coefficient. Second, it detects the contour of parallelepiped and cylindrical objects. Finally, it manipulates a parallelepiped case put on a table by sliding it on the table. Since the present robotic hand was able to perform the above three tasks with appropriate precision, we expected that it would be applicable to dexterous hands in subsequent studies.

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