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Geyong Min

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7 papers
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7

AAAI Conference 2026 Conference Paper

Achieving Equilibrium Under Utility Heterogeneity: An Agent-Attention Framework for Multi-Agent Multi-Objective Reinforcement Learning

  • Zhuhui Li
  • Chunbo Luo
  • Liming Huang
  • Luyu Qi
  • Geyong Min

Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic exploration, autonomous traffic management, and sensor network optimisation. MAMOS enhances scalability and robustness through decentralised control and more accurately captures inherent trade-offs between conflicting objectives. In MAMOS, each agent uses utility functions that map return vectors to scalar values. Existing MAMOS optimisation methods face significant challenges in handling heterogeneous objective and utility function settings, where training non-stationarity is intensified due to private utility functions and the associated policies. In this paper, we first theoretically prove that direct access to, or structured modeling of, global utility functions is necessary to achieve the Bayesian Nash Equilibrium under decentralised execution constraints. To access the global utility functions while preserving the decentralised execution, we propose an Agent-Attention Multi-Agent Multi-Objective Reinforcement Learning (AA-MAMORL) framework. Our approach implicitly learns a joint belief over other agents’ utility functions and their associated policies during centralised training, effectively mapping global states and utilities to each agent's policy. During execution, each agent independently selects actions based on local observations and its private utility function to approximate a BNE, without relying on inter-agent communication. We evaluate our framework through extensive experiments in a custom-designed MAMO Particle environment and the standard MOMALand benchmark. The results demonstrate that accessibility to global preferences and our proposed AA-MAMORL significantly improves performance and consistently outperforms state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

High-Fidelity Road Network Generation with Latent Diffusion Models

  • Jinming Wang
  • Hongkai Wen
  • Geyong Min
  • Man Luo

Road networks are the vein of modern cities. Yet, maintaining up-to-date and accurate road network information is a persistent challenge, especially in areas with rapid urban changes or limited surveying resources. Crowdsourced trajectories, e. g. , from GPS records collected by mobile devices and vehicles, have emerged as a powerful data source for continuously mapping the urban areas. However, the inherent noise, irregular and often sparse sampling rates, and the vast variability in movement patterns make the problem of road network generation from trajectories a non-trivial task. Existing methods often approach this from an appearance-based perspective: they typically render trajectories as 2D density maps and then employ heuristic algorithms to extract road networks - leading to inevitable information loss and thus poor performance especially when trajectories are sparse or ambiguities present, e. g. flyovers. In this paper, we propose a novel approach, called GraphWalker, to generate high-fidelity road network graphs from raw trajectories in an end-to-end manner. We achieve this by designing a bespoke latent diffusion transformer T2W-DiT, which treats input trajectories as generation conditions, and gradually denoises samples from a latent space to obtain the corresponding walks on the underlying road network graph - then assemble them together as the final road network. Extensive experiments on multiple datasets demonstrate the proposed GraphWalker can effectively generate high quality road networks from noisy and sparse trajectories, showcasing significant improvements over state-of-the-art.

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NeurIPS Conference 2024 Conference Paper

The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions

  • Zheng Wang
  • Geyong Min
  • Wenjie Ruan

The implicit bias of gradient descent has long been considered the primary mechanism explaining the superior generalization of over-parameterized neural networks without overfitting, even when the training error is zero. However, the implicit bias toward adversarial robustness has rarely been considered in the research community, although it is crucial for the trustworthiness of machine learning models. To fill this gap, in this paper, we explore whether consecutive layers collaborate to strengthen adversarial robustness during gradient descent. By quantifying this collaboration between layers using our proposed concept, co-correlation, we demonstrate a monotonically increasing trend in co-correlation, which implies a decreasing trend in adversarial robustness during gradient descent. Additionally, we observe different behaviours between narrow and wide neural networks during gradient descent. We conducted extensive experiments that verified our proposed theorems.

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AAAI Conference 2018 Short Paper

Towards Experienced Anomaly Detector Through Reinforcement Learning

  • Chengqiang Huang
  • Yulei Wu
  • Yuan Zuo
  • Ke Pei
  • Geyong Min

This abstract proposes a time series anomaly detector which 1) makes no assumption about the underlying mechanism of anomaly patterns, 2) refrains from the cumbersome work of threshold setting for good anomaly detection performance under specific scenarios, and 3) keeps evolving with the growth of anomaly detection experience. Essentially, the anomaly detector is powered by the Recurrent Neural Network (RNN) and adopts the Reinforcement Learning (RL) method to achieve the self-learning process. Our initial experiments demonstrate promising results of using the detector in network time series anomaly detection problems.

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TIST Journal 2017 Journal Article

UMCR

  • Hao Yin
  • Wei Wang
  • Xu Zhang
  • Yongqiang Lyu
  • Geyong Min
  • Dongchao Guo

Although mobile application ecosystems have experienced tremendous growth in recent years, retrieving content of mobile applications that serves a key to mobile content search engines still faces grand challenges. Compared to web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of applications and the lack of Uniform Resource Locator indices. In this study, we propose and implement a <underline>u</underline>ser interaction-driven <underline>m</underline>obile <underline>c</underline>ontent <underline>r</underline>etrieval (UMCR) system to address such issues, which is the first mobile content crawler in the current literature. UMCR is a distributed system that contains many measurement nodes, each of which combines the user interaction path traversing (UIPT) and Deep Package Inspection (DPI) together to obtain mobile content. UIPT determines the events of user interactions in various applications to capture the static content such as text and images, in which a traversal depth termination scheme and an optional cut-off component are adopted to balance the content coverage and traversing efficiency. Meanwhile, the analysis based on DPI is responsible for extracting the videos as well as digging the infrastructural information and performance metrics. In addition, a distributed traversal scheduling method is designed for UIPT tasks to improve the throughput and scalability in large-scale content retrieval. Experiments on retrieving content of 64 real mobile applications demonstrate that UMCR can handle diverse mobile applications efficiently. The scheduler can improve throughput by 3 times compared to the legacy arbitrary task assignment strategy.

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EAAI Journal 2012 Journal Article

Advanced issues in artificial intelligence and pattern recognition for intelligent surveillance system in smart home environment

  • Seungmin Rho
  • Geyong Min
  • Weifeng Chen

During the last decades, many researchers in image processing and AI community have been focused on developing image and video analysis and understanding. However, despite their extensive efforts on these, there are still several significant challenges such as robust object segmentation and tracking, motion feature extraction, context modeling, and machine learning algorithms. Therefore, more advanced related issues should be taken into account. We have selected nine research papers whose topics are strongly related to the intelligent surveillance system in smart home environment.

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v2026.09.13