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arXiv 2025年

作者： Chen, Yilun Wang, Wenjia China Data Science and Analytics Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China

The Certainty Equivalent (CE) heuristic is a widely-used algorithm for various dynamic resource allocation problems in OR and OM. Despite its popularity, existing theoretical guarantees of CE are limited to settings satisfying restrictive fluid regularity conditions, particularly, the non-degeneracy conditions, under the widely held belief that the violation of such conditions leads to performance deterioration and necessitates algorithmic innovation beyond CE. In this work, we conduct a refined performance analysis of CE within the general framework of online linear programming. We show that CE achieves uniformly near-optimal regret (up to a polylogarithmic factor in T) under only mild assumptions on the underlying distribution, without relying on any fluid regularity conditions. Our result implies that, contrary to prior belief, CE effectively beats the curse of degeneracy for a wide range of problem instances with continuous conditional reward distributions, highlighting the distinction of the problem’s structure between discrete and non-discrete settings. Our explicit regret bound interpolates between the mild (log T)2 regime and the worst-case √T regime with a parameter β quantifying the minimal rate of probability accumulation of the conditional reward distributions, generalizing prior findings in the multisecretary setting. To achieve these results, we develop novel algorithmic analytical techniques. Drawing tools from the empirical processes theory, we establish strong concentration analysis of the solutions to random linear programs, leading to improved regret analysis under significantly relaxed assumptions. These techniques may find potential applications in broader online and offline stochastic decision-making contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

arXiv

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arXiv 2025年

作者： Peng, Zengqi Wang, Yubin Han, Xu Zheng, Lei Ma, Jun Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low sample efficiency in complex environments continue to impede the development of safe and effective driving policies. To tackle these issues, we introduce LearningFlow, an innovative automated policy learning workflow tailored to urban driving. This framework leverages the collaboration of multiple large language model (LLM) agents throughout the RL training process. LearningFlow includes a curriculum sequence generation process and a reward generation process, which work in tandem to guide the RL policy by generating tailored training curricula and reward functions. Particularly, each process is supported by an analysis agent that evaluates training progress and provides critical insights to the generation agent. Through the collaborative efforts of these LLM agents, LearningFlow automates policy learning across a series of complex driving tasks, and it significantly reduces the reliance on manual reward function design while enhancing sample efficiency. Comprehensive experiments are conducted in the high-fidelity CARLA simulator, along with comparisons with other existing methods, to demonstrate the efficacy of our proposed approach. The results demonstrate that LearningFlow excels in generating rewards and curricula. It also achieves superior performance and robust generalization across various driving tasks, as well as commendable adaptation to different RL algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Incentive Mechanism Design for Federated Learning With Dynamic Network Pricing

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IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Gao, Lin Huang, Jianwei Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou China Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen Shenzhen518172 China

Federated learning protects users' data privacy by sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under three interaction structures of the participants. We show that the coordinated structure is better than the two uncoordinated structures, as it avoids the selfish behaviors of the network operator and the server;the vertically uncoordinated structure is better than the horizontally uncoordinated structure, as it avoids the interests misalignment between the server and the network operator. We also propose multi-period network pricing to reduce the implementation complexity of dynamic pricing. Numerical results based on real-world datasets show that our proposed mechanisms decrease the server's cost by up to 24.87% and increase the network operator's profit by up to 1245.25%, compared with the state-of-the-art benchmarks. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Incentivized Federated Learning and Unlearning

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IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Sun, Zhenyu Wei, Ermin Berry, Randall Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou511453 China Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Northwestern University Electrical and Computer Engineering Department Industrial Engineering and Management Sciences Department EvanstonIL60208 United States

To protect users' right to be forgotten in federated learning, federated unlearning aims at eliminating the impact of leaving users' data on the global learned model. The current research in federated unlearning mainly concentrates on developing effective and efficient unlearning techniques. However, the issue of incentivizing valuable users to remain engaged and preventing their data from being unlearned is still under-explored, yet important to the unlearned model performance. This paper focuses on the incentive issue and develops an incentive mechanism for federated learning and unlearning. We first characterize the leaving users' impact on the global model accuracy and the required communication rounds for unlearning. Building on these results, we propose a four-stage game to capture the interaction and information updates during the learning and unlearning process. A key contribution is to summarize users' multi-dimensional private information into one-dimensional metrics to guide the incentive design. Interestingly, we prove that allowing federated unlearning can result in reduced payoffs for both the server and users, compared to a scenario without unlearning. Numerical results demonstrate the necessity of unlearning incentives for retaining valuable leaving users, and also show that our proposed mechanisms decrease the server's cost by up to 53.91% compared to state-of-the-art benchmarks. © 2002-2012 IEEE.

关键词： federated learning federated unlearning incentive mechanism

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Sparse Additive Contextual Bandits: A Nonparametric Approach for Online Decision-making with High-dimensional Covariates

arXiv

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arXiv 2025年

作者： Wang, Wenjia Zhang, Qingwen Zhang, Xiaowei Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Hong Kong Department of Industrial Engineering and Decision Analytics The Hong Kong University of Science and Technology Hong Kong

Personalized services are central to today’s digital landscape, where online decision-making is commonly formulated as contextual bandit problems. Two key challenges emerge in modern applications: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate relationships. We address these challenges by developing a contextual bandit algorithm based on sparse additive reward models in reproducing kernel Hilbert spaces. We establish statistical properties of the doubly penalized method applied to random regions, introducing novel analyses under bandit feedback. Our algorithm achieves sublinear cumulative regret over the time horizon T while scaling logarithmically with covariate dimensionality d. Notably, we provide the first regret upper bound with logarithmic growth in d for nonparametric contextual bandits with high-dimensional covariates. We also establish a lower bound, with the gap to the upper bound vanishing as smoothness increases. Extensive numerical experiments demonstrate our algorithm’s superior performance in high-dimensional settings compared to existing approaches. Copyright © 2025, The Authors. All rights reserved.

关键词： Vector spaces

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Evolutionary Transfer Neural Architecture Search Across Spaces via Representation Learning

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hou, Boyu Feng, Liang Chen, Xuefeng Tang, Jing Tan, Kay Chen Liao, Xiaofeng Chongqing University College of Computer Science China Chongqing Key Laboratory of Big Data Intelligence and Privacy Computing Chongqing400044 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong999077 Hong Kong Data Science and Analytics Thrust Guangzhou511453 China

Neural Architecture Search (NAS) has emerged as a crucial method for automating the design of deep learning models. Despite its potential, NAS frequently requires substantial computational and hardware resources. To mitigate these challenges, transferable NAS (TNAS) has been introduced, leveraging prior NAS results to enhance performance on new tasks. However, existing methods largely focus on knowledge transfer within identical neural search spaces, overlooking the potential for cross-domain transferability. Motivated by this gap, we explore evolutionary TNAS across heterogeneous search spaces by learning common neural representations. In particular, we introduce a novel approach that encodes both operational and topological information of neural architectures into a unified sequence using a simple tokenizer. This sequence is then processed by a variational auto-encoder, with a Transformer-based encoder to capture rich neural representations and a decoder that reconstructs the original sequence. By utilizing these latent representations, we further establish an inter-domain mapping that acts as a bridge, enabling effective explicit solution transfer among diverse search spaces to enhance the evolutionary NAS process. To harness this capability, we develop an evolutionary sequential transfer optimization approach that transfers knowledge during population initialization, providing both flexibility and adaptability. To the best of our knowledge, this work serves as the first attempt in the literature exploring evolutionary TNAS across diverse spaces. Moreover, we demonstrate the utility of our method through comprehensive empirical studies using different architecture spaces, including NAS-Bench-101, NAS-Bench-201, and the DARTS search space. Our results show that the proposed method significantly enhances the adaptability and performance of NAS across varied domains. © 1997-2012 IEEE.

关键词： Transfer learning

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EditFollower: Tunable Car Following Models for Customizable Driving Behavior

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Chen, Xianda Han, Xu Zhu, Meixin Chu, Xiaowen Tiu, PakHin Zheng, Xinhu Wang, Yinhai The Hong Kong University of Science and Technology (Guangzhou) Intelligent Transportation Thrust Systems Hub Guangzhou 511455 China The Hong Kong University of Science and Technology (Guangzhou) Data Science and Analytics Thrust Information Hub Guangzhou 511455 China Southeast University School of Transportation Nanjing 211189 China University of Washington Department of Civil and Environmental Engineering Seattle 98195 WA United States

In the realm of driving technologies, fully autonomous vehicles have not been widely adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing driving experiences. Among these, car-following behavior modeling plays a pivotal role, forming the foundation for systems that ensure safe and efficient vehicle interactions. However, current approaches often rely on fixed parameters, failing to capture the diverse social preferences and driving styles of individuals. To overcome these limitations, we propose the Editable Behavior Generation (EBG) model, a data-driven car-following model that allows for adjusting driving discourtesy levels. The framework integrates diverse courtesy calculation methods into long short-term memory (LSTM) and Transformer architectures, offering a comprehensive approach to capture nuanced driving dynamics. By integrating various discourtesy values during the training process, our model generates realistic agent trajectories with different levels of courtesy in car-following behavior. Experimental results on the naturalistic datasets showcase a reduction in Mean Squared Error (MSE) of spacing and MSE of speed compared to baselines, establishing style controllability. To the best of our knowledge, this work represents the first data-driven car-following model capable of dynamically adjusting discourtesy levels. Our model provides valuable insights for the development of ADAS that take into account drivers’ social preferences. © 2000-2011 IEEE.

关键词： advanced driver assistance systems Autonomous vehicles car-following driving behavior

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Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15200-15214页

作者： Song, Yaoxian Sun, Penglei Jin, Piaopiao Ren, Yi Zheng, Yu Li, Zhixu Chu, Xiaowen Zhang, Yue Li, Tiefeng Gu, Jason Zhejiang University Center for X-Mechanics School of Aeronautics and Astronautics Zhejiang Hangzhou China Information Hub Data Science and Analytics Thrust Guangdong China Huawei Technologies Advanced Manufacturing Lab Guangdong Shenzhen China UBTECH Robotics Inc Research Institute Guangdong Shenzhen China Renmin University of China School of Information School of Smart Governance Beijing China Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study School of Engineering Zhejiang Hangzhou China Dalhousie University Department of Electrical and Computer Engineering HalifaxB3H 4R2 Canada

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can be seen as atomic elements to compose an object, which contains rich semantic knowledge and a strong correlation with affordance. However, lacking a large part-wise 3D robotic dataset limits the development of part representation learning and downstream applications. In this paper, we propose a new large Language-guided SHape grAsPing datasEt (named LangSHAPE) to promote 3D part-level affordance and grasping ability learning. From the perspective of robotic cognition, we design a two-stage fine-grained robotic grasping framework (named LangPartGPD), including a novel 3D part language grounding model and a partaware grasp pose detection model, in which explicit language input from human or large language models (LLMs) could guide a robot to generate part-level 6-DoF grasping pose with textual explanation. Our method combines the advantages of humanrobot collaboration and LLMs’ planning ability using explicit language as a symbolic intermediate. To evaluate the effectiveness of our proposed method, we perform 3D part grounding and fine-grained grasp detection experiments on both simulation and physical robot settings, following language instructions across different degrees of textual complexity. Results show our method achieves competitive performance in 3D geometry fine-grained grounding, object affordance inference, and 3D part-aware grasping tasks. © 2004-2012 IEEE.

关键词： Visual languages

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EnsembleFollower: A Hybrid Car-Following Framework Based on Hierarchical Planning and Reinforcement Learning

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IEEE Transactions on Vehicular Technology 2025年

作者： Han, Xu Chen, Xianda Zhu, Meixin Cai, Pinlong Zhou, Jianshan Chu, Xiaowen Data Science and Analytics Thrust Information Hub Guangzhou China Intelligent Transportation Thrust Systems Hub Guangzhou China Sensing and Computation for Ubiquitous Internet of Things Guangdong Provincial Key Lab of Integrated Communication China Southeast University School of Transportation Nanjing211189 China Shanghai Artificial Intelligence Laboratory Shanghai China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety Control State Key Laboratory of Intelligent Transportation Systems China Beihang University School of Transportation Science and Engineering Beijing100191 China

Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as they cannot fully capture the complexity inherent in car-following processes, or may struggle in unseen scenarios due to their reliance on confined driving skills present in training data. It is worth noting that each car-following model possesses its own strengths and weaknesses depending on specific driving scenarios. Therefore, we propose EnsembleFollower, a closed-loop hierarchical planning framework for achieving human-like autonomous car-following. The EnsembleFollower framework involves a high-level Reinforcement Learning-based agent responsible for judiciously managing multiple low-level models (such as Intelligent Driver Model and Gipps model) according to the current state, either by selecting an appropriate car-following model to perform an action or by allocating different weights across all primitive models. Moreover, we propose a jerk-constrained kinematic model for more convincing microscopic traffic simulations. We evaluate the proposed method based on real-world driving data from the HighD dataset. The experimental results illustrate that EnsembleFollower yields an improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse driving conditions by leveraging the strengths of various car-following models. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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A dataset for Investigations of Amine-Impregnated Solid Adsorbent for Direct Air Capture

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Scientific data 2025年第1期12卷 1-24页

作者： Wang, Eryu Luo, Liping Wang, Jiachuan Dai, Jiaxin Li, Shuangyin Chen, Lei Li, Jia Innovation Policy and Entrepreneurship Thrust Society Hub The Hong Kong University of Science and Technology (Guangzhou) No.1 Duxue Road Nansha Guangzhou 511453 China Data Science and Analytics Thrust Information Hub The Hong Kong University of Science and Technology (Guangzhou) No.1 Duxue Road Nansha Guangzhou 511453 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clearwater Bay Kowloon 999077 Hong Kong School of Computer Science South China Normal University Guangzhou China School of Interdisciplinary Studies Lingnan University Tuen Mun Hong Kong

Amine-impregnated solid adsorbents are widely explored for point source capture and direct air capture (DAC) to address climate change. Existing literature serves as a valuable source for the investigation of amine-functionalized solid adsorbents. This study selected 52 articles from bibliographic platforms using GPT-assisted data source screening. A total of 1,336 data points were manually collected. Each data point is characterized by 28 features including the CO₂ capture performance of various adsorbents from diluted to concentrated sources, resulting in 29,857 records. The methodology addresses inconsistencies in units and terminologies in the published articles and demonstrates database reliability, regularity and integrity through statistical analysis. The diverse types of amines and mesoporous solids in the database offer innovation potential for future research. In addition, two machine learning models were trained to promote dataset reuse by scientists from lab-based research and cheminformatics. This study provides opportunities to explore the use of machine learning on small databases and encourages data sharing and uniform reporting among DAC communities. © The Author(s) 2025.

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