1. 9.7 Learning with Language-Guided State Abstractions
   • Authors: Andi Peng, Ilia Sucholutsky, Belinda Z. Li, Theodore R. Sumers, Thomas L. Griffiths, Jacob Andreas, Julie A. Shah
   • Reason: This paper presents LGA, a novel method that leverages natural language to automatically build state abstractions for imitation learning in high-dimensional observation spaces. Combining language models with state representation learning could mark a significant advancement in generalizability, and the inclusion of high-profile authors like Thomas L. Griffiths and Jacob Andreas suggests strong potential for influence.
2. 9.5 Disentangling the Causes of Plasticity Loss in Neural Networks
   • Authors: Clare Lyle, Zeyu Zheng, Khimya Khetarpal, Hado van Hasselt, Razvan Pascanu, James Martens, Will Dabney
   • Reason: The paper addresses a fundamental question in reinforcement learning related to the loss of plasticity, offering insights into maintaining the trainability of neural networks. The paper’s excellent author lineup, including experts like Hado van Hasselt and Razvan Pascanu, lends authority to the findings, ensuring a high impact.
3. 9.2 FORML: A Riemannian Hessian-free Method for Meta-learning with Orthogonality Constraint
   • Authors: Hadi Tabealhojeh, Soumava Kumar Roy, Peyman Adibi, Hossein Karshenas
   • Reason: This paper proposes a computational method improvement for meta-learning, where computation is typically heavy due to Riemannian backpropagation. The innovation could lead to wider applications of meta-learning, making this work potentially influential within the domain of optimization and machine learning.
4. 9.0 ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
   • Authors: Shiqi Lei, Kanghoon Lee, Linjing Li, Jinkyoo Park, Jiachen Li
   • Reason: The paper introduces ELA, a new approach for offline learning in the context of zero-sum games. Given that zero-sum games are a fundamental concept in both economics and AI, the paper could have broad appeal. Although the authors might not be as widely renowned as others, the topic’s relevance suggests potential for substantial impact.
5. 8.9 Stochastic contextual bandits with graph feedback: from independence number to MAS number
   • Authors: Yuxiao Wen, Yanjun Han, Zhengyuan Zhou
   • Reason: This work delves into contextual bandits with graph feedback, a vital area for interactive learning with applications in auctions and inventory control. It may be influential in shaping the understanding of statistical complexity within interactive learning systems. The quality of the underlying theoretical work suggests potential influence, even if the authors have yet to build strong authority in the field.