The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios, known as the simulation-to-reality (sim-to-real) gap. This gap is particularly challenging in spaceflight, where real-world training data are scarce due to high cost and limited planetary exploration data. Traditional approaches, such as system identification and synthetic data generation, depend on sufficient data and often fail due to modeling assumptions or lack of physics-based constraints. We propose addressing this data scarcity by introducing physics-based learning bias in a generative model. Specifically, we develop the Mutual Information-based Split Variational Autoencoder (MI-VAE), a physics-informed VAE that learns differences between observed system trajectories and those predicted by physics-based models. The latent space of the MI-VAE enables generation of synthetic datasets that respect physical constraints. We evaluate MI-VAE on a planetary lander problem, focusing on limited real-world data and offline RL training. Results show that augmenting datasets with MI-VAE samples significantly improves downstream RL performance, outperforming standard VAEs in statistical fidelity, sample diversity, and policy success rate. This work demonstrates a scalable strategy for enhancing autonomous controller robustness in complex, data-constrained environments.
Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；The State Key Laboratory of Integrated Services Networks（综合业务网络国家重点实验室）；School of Network and Information Security, Xidian University（西安电子科技大学网络与信息安全学院）
Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients
Concordia University（康考迪亚大学）；Quantact Laboratory（Quantact实验室）；Centre de Recherches Mathématiques（数学研究中心）；Digital Insurance And Long term risk (DIALog) research chair（数字保险与长期风险（DIALog）研究讲席）
FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcity
Department of Computer Science University of the Western Cape（计算机科学系乌得勒支大学）；Department of Computer Science Missouri University of Science and Technology（计算机科学系密苏里科技大学）
The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
College of Computing, Georgia Institute of Technology（佐治亚理工学院计算学院）
University of Texas at Dallas（德克萨斯大学达拉斯分校）；Purdue University（普渡大学）
In today's society, AI systems are increasingly used to make critical decisions such as credit scoring and patient triage. However, great convenience brought by AI systems comes with troubling prevalence of bias against underrepresented groups. Mitigating bias in AI systems to increase overall fairness has emerged as an important challenge. Existing studies on mitigating bias in AI systems focus on eliminating sensitive demographic information embedded in data. Given the temporal and contextual complexity of conceptualizing fairness, lossy treatment of demographic information may contribute to…
Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspective
Institute for Intelligent Networked Systems, Northeastern University London（智能网络系统研究所，诺思安普顿大学伦敦分校）；Departments of Electrical and Computer Engineering and of Computer Science at the Technion–Israel Institute of Technology（技术ion-以色列理工学院电子与计算机工程系和计算机科学系）
We design a metric learning approach that aims to address computational challenges that yield from modeling human outcomes from ambulatory real-life data. The proposed metric learning is based on a Siamese neural network (SNN) that learns the relative difference between pairs of samples from a target user and non-target users, thus being able to address the scarcity of labelled data from the target. The SNN further minimizes the Wasserstein distance of the learned embeddings between target and non-target users, thus mitigating the distribution mismatch between the two. Finally, given the fact …
AI-driven Generation of MALDI-TOF MS for Microbial Characterization
Department of Signal Theory and Communications（信号理论与通信系）；Universidad Carlos III de Madrid（卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)（格雷戈里·马兰农卫生研究院）
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
Deakin University（迪肯大学）；Data61, CSIRO（联邦科学与工业研究组织Data61研究院）；The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；University of New South Wales (UNSW)（新南威尔士大学）
