Yakun Wang

NeurIPS Conference 2025 Conference Paper

Direct Fisher Score Estimation for Likelihood Maximization

• Sherman Khoo
• Yakun Wang
• Song Liu
• Mark Beaumont

We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based optimization method that directly models the Fisher score based on a local score matching technique which uses simulations from a localized region around each parameter iterate. By employing a linear parameterization for the surrogate score model, our technique admits a closed-form, least-squares solution. This approach yields a fast, flexible, and efficient approximation to the Fisher score, effectively smoothing the likelihood objective and mitigating the challenges posed by complex likelihood landscapes. We provide theoretical guarantees for our score estimator, including bounds on the bias introduced by the smoothing. Empirical results on a range of synthetic and real-world problems demonstrate the superior performance of our method compared to existing benchmarks.

AAAI Conference 2025 Conference Paper

Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation

• Wei Chen
• Guo Ye
• Yakun Wang
• Zhao Zhang
• Libang Zhang
• Daixin Wang
• Zhiqiang Zhang
• Fuzhen Zhuang

Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, often overlooking structural shifts, resulting in limited effectiveness when addressing structurally complex transfer scenarios. Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts in node embeddings, thereby reducing the effectiveness of knowledge transfer. To address this issue, we introduce a novel approach for UGDA called Target-Domain Structural Smoothing (TDSS). TDSS is a simple and effective method designed to perform structural smoothing directly on the target graph, thereby mitigating structural distribution shifts and ensuring the consistency of node representations. Specifically, by integrating smoothing techniques with neighbor- hood sampling, TDSS maintains the structural coherence of the target graph while mitigating the risk of over-smoothing. Our theoretical analysis shows that TDSS effectively reduces target risk by improving model smoothness. Empirical results on three real-world datasets demonstrate that TDSS outperforms recent state-of-the-art baselines, achieving significant improvements across six transfer scenarios.

EAAI Journal 2020 Journal Article

Structural hole-based approach to control public opinion in a social network

• Cheng Gong
• YaJun Du
• XianYong Li
• XiaoLiang Chen
• Xiaoying Li
• Yakun Wang
• Qiaoyu Zhou

ECAI Conference 2016 Conference Paper

Topic-Level Influencers Identification in the Microblog Sphere

• Yakun Wang
• Zhongbao Zhang
• Sen Su
• Cheng Chang
• Muhammad Azam Zia

This paper studies the problem of identifying influencers on specific topics in the microblog sphere. Prior works usually use the cumulative number of social links to measure users' topic-level influence, which ignores the dynamics of influence. As a result, they usually find faded influencers. To address the limitations of prior methods, we propose a novel probabilistic generative model to capture the variation of influence over time. Then a influence decay method is proposed to measure users' current topic-level influence.