AHRL-PM: Asynchronous Hierarchical Reinforcement Learning Framework for Enhanced Portfolio Management [0.03%]
异步分层强化学习框架在增强型组合管理中的应用 AHRL-PM
Shuyue Liu,Tianxiang Cui,Yiran Li et al.
Shuyue Liu et al.
Effective portfolio management (PM) is a cornerstone of financial strategy, yet it is often challenged by the uncertainties and the high dimensionality of market data. Traditional PM techniques, whether model-based or model-free, frequently...
Cheng Zhou,Xin Man,Congshan Ma et al.
Cheng Zhou et al.
This article addresses the multinode cooperative jamming problem in communication networks with unknown topology. To overcome the combinatorial explosion in decision-making and the credit assignment challenge under full-bandit feedback, we ...
OpenMvL: An Attribute-Inspired Dual-Head Framework for Open-Set Multiview Learning [0.03%]
开放多视图学习的属性启发式双头框架
Shide Du,Zihan Fang,Lan Du et al.
Shide Du et al.
Numerous researchers have sought to integrate multiview data to enhance task performance. However, existing multiview methods are primarily designed for closed-set environments with fully known classes. In real-world open-set scenarios, the...
Agent4CD: Generative Agent for Cognitive Diagnosis in Intelligent Education [0.03%]
基于智能教育的认知诊断生成智能体Agent4CD
Xinjie Sun,Qi Liu,Weiyin Gong et al.
Xinjie Sun et al.
Cognitive diagnosis (CD) is a central assessment approach in intelligent education that aims to uncover learners' knowledge mastery and latent cognitive abilities under examination conditions. However, existing models primarily infer learne...
FuGuard: Client-Level Federated Unlearning via Generative Surrogates and Optimal Transport [0.03%]
FuGuard：通过生成代理和最优传输实现客户端级别的联合遗忘机制
Pian Qi,Daniela Annunziata,Chiara Jappelli et al.
Pian Qi et al.
Federated learning (FL) is a widely adopted paradigm that enables collaborative model training while preserving data privacy. As concerns around data poisoning and the "right to be forgotten" continue to grow, federated unlearning, which is...
Reinforcement Learning in Pursuit-Evasion Differential Game: Safety, Stability, and Robustness [0.03%]
追逃微分博弈中的强化学习：安全性、稳定性与鲁棒性
Xinyang Wang,Hongwei Zhang,Jun Xu et al.
Xinyang Wang et al.
Safety and stability are two critical concerns in pursuit-evasion (PE) problems in an obstacle-rich environment. Most existing works combine control barrier functions (CBFs) and reinforcement learning (RL) to provide an efficient and safe s...
PMSN: A Parallel Multi-Compartment Spiking Neuron for Multiscale Temporal Processing [0.03%]
一种用于多层次时间处理的并行多隔室脉冲神经元模型(PMSN)
Xinyi Chen,Jibin Wu,Chenxiang Ma et al.
Xinyi Chen et al.
Spiking neural networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multiscale temporal processing compared to their biological counterpar...
Output Tracking of Periodically Time-Varying Boolean Networks: State-Flipped Control and Q-Learning Approaches [0.03%]
周期时间varying布尔网络的输出跟踪：状态翻转控制与Q学习方法
Xingyu Ge,Amol Yerudkar,Jianquan Lu et al.
Xingyu Ge et al.
This article investigates the output tracking problem for periodically time-varying Boolean networks (PTVBNs), motivated by rhythmic gene regulation and cyclic operating regimes in discrete systems. In such networks, the update rules change...
Open-Set Domain Adaptation via Free Boundary Optimal Transport With Support Control [0.03%]
基于支持控制的自由边界最优传输开放集领域适应方法
Zi-Xian Huang,Chuan-Xian Ren
Zi-Xian Huang
Optimal transport (OT) has proven highly successful in various machine learning tasks, primarily by measuring distributional differences. As a distance-driven method, OT heavily relies on the underlying distance structure of the sample spac...
Dual Joint Covariance Alignment Method for Incomplete Data Classification [0.03%]
用于不完整数据分类的双重联合协方差对齐方法
Linqing Huang,Gongshen Liu
Linqing Huang
For incomplete data classification, the missing values are usually imputed by different estimation methods to make the data complete. In practice, the estimated values are not real attribute values, so the distributions of imputed training ...