1. 8.9 Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks
   • Authors: Shufan Wang, Guojun Xiong, Shichen Zhang, Huacheng Zeng, Jian Li, Shivendra Panwar
   • Reason: This paper proposes a structured reinforcement learning solution that is computationally efficient and has demonstrated significant gains over existing approaches in realistic network simulations, making it highly relevant for practical applications in telecommunications.
2. 8.9 An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging
   • Authors: Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
   • Reason: This paper addresses the important issue of explainability in deep reinforcement learning applied to a critical healthcare application, which is likely to have substantial influence in medical AI applications.
3. 8.7 Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
   • Authors: Maeva Guerrier, Hassan Fouad, Giovanni Beltrame
   • Reason: A comprehensive review of control barrier functions within safe reinforcement learning could have a significant impact on the practical application of RL in robotics, enhancing safety and efficacy.
4. 8.6 Making Better Use of Unlabelled Data in Bayesian Active Learning
   • Authors: Freddie Bickford Smith, Adam Foster, Tom Rainforth
   • Reason: The paper offers a novel semi-supervised framework for Bayesian active learning, which could significantly improve the efficiency of learning, hence it has the potential to impact active learning methodologies.
5. 8.5 Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
   • Authors: Zhuoyuan Wang, Haoming Jing, Christian Kurniawan, Albert Chern, Yorie Nakahira
   • Reason: This paper introduces a novel technique for designing safety certificates in stochastic systems, which is crucial for advancing reinforcement learning in the context of safety-critical applications. However, there is text overlap with prior work, which may slightly affect its innovation score.
6. 8.3 Closing the gap: Optimizing Guidance and Control Networks through Neural ODEs
   • Authors: Sebastien Origer, Dario Izzo
   • Reason: This work enhances the accuracy of Guidance & Control Networks (G&CNETs) using Neural ODEs, which can significantly improve the reliability of autonomous control policies in spacecraft.
7. 8.3 Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
   • Authors: Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay
   • Reason: Proposes a new framework that could enhance the performance of graph neural networks, thereby impacting the field of representation learning which is essential in many machine learning applications.
8. 8.1 IDIL: Imitation Learning of Intent-Driven Expert Behavior
   • Authors: Sangwon Seo, Vaibhav Unhelkar
   • Reason: Addresses the important aspect of capturing and mimicking expert intent-driven behaviors, which can improve the fidelity of trained models in reinforcement learning applications, especially for human-agent interactions.
9. 8.0 Cycling into the workshop: predictive maintenance for Barcelona’s bike-sharing system
   • Authors: Jordi Grau-Escolano, Aleix Bassolas, Julian Vicens
   • Reason: The paper addresses the practical issue of predictive maintenance using machine learning, which has clear implications for urban mobility and could influence the maintenance strategies for physical systems.
10. 7.8 A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories
    • Authors: Niki van Stein, Sarah L. Thomson, Anna V. Kononova
    • Reason: This analysis of structural bias in evolutionary strategies algorithms can impact the understanding and development of more robust optimization algorithms in machine learning, especially in reinforcement learning settings.