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Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards

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arXiv 2023年

作者： Xue, Bo Wang, Yimu Wan, Yuanyu Yi, Jinfeng Zhang, Lijun Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen China Cheriton School of Computer Science University of Waterloo Waterloo Canada School of Software Technology Zhejiang University Ningbo China JD AI Research Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Peng Cheng Laboratory Shenzhen China

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose (1 + ϵ)-th moment is bounded for some ϵ ∈ (0, 1]. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of Oe(dT 1+ 1 ϵ ), where d is the dimension of contextual information and T is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only O(log T) rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when ϵ = 1. Numerical experimental results confirm the merits of our algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Privacy-Preserving Multi-Task Online Matching in Mobile Crowdsensing: A Snapshot-Based Approach

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IEEE Internet of Things Journal 2025年

作者： Wei, Kaimin Zhao, Shiting Chen, Jinpeng Pei, Tingrui Sun, Dezhi Jinan University College of Information Science and Technology China Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Information Science China

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms. © 2014 IEEE.

关键词： Mobile Crowdsensing Multi-task Matching Privacy-Preserving Snapshot

Cognitive Disentanglement for Referring Multi-Object Tracking

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arXiv 2025年

作者： Liang, Shaofeng Guan, Runwei Lian, Wangwang Liu, Daizong Sun, Xiaolou Wu, Dongming Yue, Yutao Ding, Weiping Xiong, Hui Qingdao China Guangzhou China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China Wangxuan Institute of Computer Technology Peking University Beijing China School of Automation Southeast University Nanjing China School of Computer Science Beijing Institute of Technology Beijing China School of Artificial Intelligence and Computer Science Nantong University Nantong China Guangzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from the human visual processing system to RMOT tasks. Specifically, our framework first establishes cross-modal connections while preserving modality-specific characteristics. It then disentangles language descriptions and hierarchically injects them into object queries, refining object understanding from coarse to fine-grained semantic levels. Finally, we reconstruct language representations based on visual features, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling

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arXiv 2024年

作者： Liao, Haicheng Li, Yongkang Wang, Chengyue Lai, Songning Li, Zhenning Bian, Zilin Lee, Jaeyoung Cui, Zhiyong Zhang, Guohui Xu, Chengzhong State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Internet of Things for Smart City University of Macau China State Key Laboratory of Internet of Things for Smart City Departments of Civil and Environmental Engineering and Computer and Information Science University of Macau China Guangzhou China Transportation Planning and Engineering The Department of Civil and Urban Engineering New York University New York United States School of Traffic and Transportation Engineering Central South University Changsha China School of Transportation Science and Engineering Beihang University Beijing China Department of Civil and Environmental Engineering University of Hawaii HonoluluHI United States

The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA). This novel loss function, together with a multi-task learning strategy, shifts the focus of the predictive model towards the critical moments preceding an accident. We rigorously evaluate the performance of our framework on three benchmark datasets—Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), and DADA-2000 Dataset—demonstrating its superior predictive accuracy through key metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA). Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

Spatial Constraint for Efficient Semi-Supervised Video Object Segmentation

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SSRN

SSRN 2023年

作者： Chen, Yadang Ji, Chuanjun Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China Beijing100190 China

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation;the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems. © 2023, The Authors. All rights reserved.

关键词： Real time systems

Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score

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arXiv 2023年

作者： Zhang, Shuhai Liu, Feng Yang, Jiahao Yang, Yifan Li, Changsheng Han, Bo Tan, Mingkui School of Software Engineering South China University of Technology China Pazhou Laboratory China The University of Melbourne Australia Beijing Institute of Technology Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

Adversarial detection aims to determine whether a given sample is an adversarial one based on the discrepancy between natural and adversarial distributions. Unfortunately, estimating or comparing two data distributions is extremely difficult, especially in high-dimension spaces. Recently, the gradient of log probability density (a.k.a., score) w.r.t. the sample is used as an alternative statistic to compute. However, we find that the score is sensitive in identifying adversarial samples due to insufficient information with one sample only. In this paper, we propose a new statistic called expected perturbation score (EPS), which is essentially the expected score of a sample after various perturbations. Specifically, to obtain adequate information regarding one sample, we perturb it by adding various noises to capture its multi-view observations. We theoretically prove that EPS is a proper statistic to compute the discrepancy between two samples under mild conditions. In practice, we can use a pre-trained diffusion model to estimate EPS for each sample. Last, we propose an EPS-based adversarial detection (EPSAD) method, in which we develop EPS-based maximum mean discrepancy (MMD) as a metric to measure the discrepancy between the test sample and natural samples. We also prove that the EPS-based MMD between natural and adversarial samples is larger than that among natural samples. Extensive experiments show the superior adversarial detection performance of our EPS-AD. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Knowledge Concept Recommendation Model for MOOCs with Local Sub-graph Embedding

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Knowledge Concept Recommendation Model for MOOCs with Local ...

Automation, Robotics and computer Engineering (ICARCE), International Conference on

作者： ChengCheng Ju Yi Zhu ChenYu Wang Department of Computer Science and Technology Jiangsu Normal University Xuzhou China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781665475495

Massive open online courses (MOOCs) play a crucial role in modern education by providing users with largescale open online learning platforms. Substantial research has been conducted to reduce user learning blindness and improve user experience, particularly in personalized course recommendations based on graph neural networks. However, these efforts have focused primarily on fixed or homogeneous graphs, are vulnerable to data sparsity problems, and are difficult to scale. This study proposes to overcome this limitation using graph convolution on local subgraphs combined with an extended matrix factorization model. First, the proposed method decomposes the heterogeneous graph into multiple meta-path-based subgraphs and combines random wandering sampling methods to capture complex semantic relationships between entities while sampling nodes' influence-rich neighborhoods. Next, the attention mechanism adaptively fuses the contextual information of different subgraphs for a more comprehensive construction of user preferences. Experiments on publicly available MOOCs datasets reveal that the proposed model outperforms other benchmark models and is highly scalable while alleviating the data sparsity problem.

关键词： Knowledge engineering Adaptation models Electronic learning Computational modeling Semantics Sampling methods Data models

Data-mechanism-driven Product Performance Optimization with Multiple Parameters Under Uncertainties in Manufacturing Automation Systems

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Data-mechanism-driven Product Performance Optimization with ...

IEEE International Conference on Automation science and Engineering (CASE)

作者： Kaiyue Cui Zhaoxi Hong Xiuju Song Mengchu Zhou Zhiwu Li Yixiong Feng Jianrong Tan State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Ningbo Innovation Center Zhejiang University Ningbo China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Systems Engineering Macau University of Science and Technology Macau China State Key Laboratory of Public Big Data Guizhou University Guiyang China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

High-end equipment with robust and excellent performance is of great significance to manufacturing automation systems. Product parameters are the foundation of performance. Usually, there is a lack of clear physical models to reveal the relationship between parameters and performance. Simulation is an important way to understand the design results of product performance parameters. However, simulations are often particularly time and resource-consuming. To address these issues, this work proposes a data-mechanism-driven product performance optimization method, which introduces Taguchi' method, nonparametric correlation testing, and multiple attribute decision making (MADM), to efficiently obtain the optimal parameter scheme. A novel MADM method is designed, which is combined with the Multi-Objective Optimization on the basis of a Ratio Analysis plus the full MULTIplicative form (MULTIMOORA) and the Variable Neighborhood Search. It has been proven that it performs better than the MULTIMOORA based on the well-known Simulated Annealing and Tabu search. Finally, taking the cutting unit of EBZ200i as a case study, we have successfully obtained the optimal parameter scheme that comprehensively performs better in terms of wear performance, robustness, and environmental economic performance.

关键词： Uncertainty Correlation computer aided software engineering Decision making Simulated annealing Robustness Environmental economics Optimization Manufacturing automation Testing

Resource Optimization for LEO Constellation Networks: A Multi-satellite Cooperative Coverage Design

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IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization