| 研究課題/領域番号 |
23K16939
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分61030:知能情報学関連
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| 研究機関 | 北海道大学 (2024)
筑波大学 (2023) |
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研究代表者 |
NGUYEN DAIHAI 北海道大学, 情報科学研究院, 准教授 (50968401)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2024年度)
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| 配分額 *注記 |
2,730千円 (直接経費: 2,100千円、間接経費: 630千円)
2025年度: 520千円 (直接経費: 400千円、間接経費: 120千円)
2024年度: 910千円 (直接経費: 700千円、間接経費: 210千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
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| キーワード | proximal MCMC sampling / optimal transport / Bayesian inference / Generative models / Constrained domains / graph kernels / graph structured data |
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| 研究開始時の研究の概要 |
We design novel measures for graphs with the following advantages:
1) taking into account node features and global structures of graphs.
2) deriving valid kernels that can be used for kernel-based frameworks.
3) reducing the complexity of computing the pairwise distance or kernel matrices.
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| 研究実績の概要 |
Our work has focused on generating structured data while effectively incorporating prior knowledge. To address this challenge, we formulated a general optimization problem involving the minimization of a composite objective function defined over probability distribution space. The objective consists of two components: one assumed to have a variational representation, and the other expressed in terms of the expectation operator of a possibly nonsmooth convex regularizer function, which represents the prior knowledge. We develope a general framework based on the optimal transport theory to minimize this objective. Furthermore, we also provide theoretical analyses and present experimental results to showcase the effectiveness of the proposed method.
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| 現在までの達成度 |
現在までの達成度
1: 当初の計画以上に進展している
理由
We have achieved promising results in generating structured data through distributional optimization, with our findings published in leading conferences and journals. Looking ahead, we aim to expand our research to encompass graph data and its diverse applications.
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| 今後の研究の推進方策 |
We plan to continue advancing optimal transport-based frameworks for solving distributional optimization problems, with a particular emphasis on applications in Bayesian inference and graph structured data.
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