Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial challenges, especially when data is preferred to remain on the device where it was collected for reasons such as data integrity, communication bandwidth, and privacy. This paper introduces a federated quantile regression algorithm to address these challenges. Quantile regression provides a more comprehensive view of the relationship between variables than mean regression models. However, traditional approaches face difficulties when dealing with nonconvex sparse penalties and the inherent non-smoothness of the loss function. For this purpose, we propose a federated smoothing proximal gradient (FSPG) algorithm that integrates a smoothing mechanism with the proximal gradient framework, thereby enhancing both precision and computational speed. This integration adeptly handles optimization over a network of devices, each holding local data samples, making it particularly effective in federated learning scenarios. The FSPG algorithm ensures steady progress and reliable convergence in each iteration by maintaining or reducing the value of the objective function. By leveraging nonconvex penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), the proposed method can identify and preserve key predictors within sparse models. Comprehensive simulations validate the robust theoretical foundations of the proposed algorithm and demonstrate improved estimation precision and reliable convergence.
Inria, ENS de Lyon, CNRS, Université Claude Bernard Lyon 1, LIP（法国国家信息与自动化技术研究所、里昂大学、国家科学研究中心、克莱尔伯恩大学、LIP）；IRIT, Université de Toulouse, CNRS（图卢兹大学、国家科学研究中心、IRIT）
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
Indian Institute of Science（印度科学学院）
在大多数强化学习（RL）算法的初始训练迭代期间，代理执行大量随机探索步骤。在现实世界中，这可能会限制这些算法的实用性，因为它可能导致潜在的危险行为。因此，安全探索是在现实世界中应用RL算法的一个关键问题。这个问题最近在约束马尔可夫决策过程（CMDP）框架下得到了很好的研究，其中除了单阶段奖励外，代理还根据状态转换接收单阶段成本或惩罚。规定的成本函数负责将任何给定时间步的不良行为映射到标量值。然后，目标是找到一个可行的政策，最大限度地提高回报，同时限制成本回报低于规定的阈值，在训练和部署。我们提出了一种基于策略模型的安全深度RL算法，在该算法中，我们以在线方式学习环境的过渡动态，并使用基于拉格朗日松弛的近端策略优化找到可行的最优策略。我们使用不同初始化的神经网络的集成来解决环境模型学习过程中所面临的认知和任意不确定性问题。我们使用具有挑战性的安全强化学习基准-Open AI Safety Gym，将我们的方法与约束RL中的相关无模型和基于模型的方法进行比较。我们证明，我们的算法是更有效的样本，并在较低的累积危险违规的结果相比，受约束的无模型的方法。此外，我们的方法显示出更好的回报性能比其他约束的基于模型的方法在文献中。
A Noise-Robust Elicit-to-Optimize Framework for Distortion Riskmetrics via Inverse Reinforcement Learning
School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）；School of Mathematics and Statistics, Carleton University（卡尔顿大学数学与统计学院）
Stochastic Approximation Methods for Distortion Risk Measure Optimization
Guanghua School of Management Peking University（北京大学光华管理学院）；School of Business and Economics Vrije Universiteit Amsterdam（荷兰阿姆斯特丹自由大学商学院）；Department of Applied Mathematics and Statistics State University of New York at Stony Brook（纽约州立大学石溪分校应用数学与统计学系）；Guanghua School of Management School of Artificial Intelligence for Science Peking University（北京大学光华管理学院人工智能科学学院）；Xiangjiang Laboratory（湘江实验室）
