| Budget Amount *help |
¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2023: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2022: ¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2021: ¥5,070,000 (Direct Cost: ¥3,900,000、Indirect Cost: ¥1,170,000)
Fiscal Year 2020: ¥5,460,000 (Direct Cost: ¥4,200,000、Indirect Cost: ¥1,260,000)
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| Outline of Final Research Achievements |
First, with the aim of expanding the applicability of machine learning, we improved the performance of deep learning methods for graph-structured data, and developed models that are more expressive than conventional models and effective learning methods for them. In addition, with the aim of expanding the applicability of data-driven decision making, we developed causal effect estimation methods in situations where confounding variables are unknown, applied causal effect estimation to the field of chemistry, and developed predictive modeling methods for small data. Furthermore, we combined graph deep learning and causal inference to develop causal effect estimation for interventions with graph structure and causal effect estimation methods on graphs.
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