LeArning Abstract
Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.
Papers
Learning to learn ecosystems from limited data -- a meta-learning approach
Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Can We Delegate Learning to Automation?: A Comparative Study of LLM Chatbots, Search Engines, and Books
Yeonsun Yang, Ahyeon Shin, Mincheol Kang, Jiheon Kang, Jean Young Song
OCC-MLLM-Alpha:Empowering Multi-modal Large Language Model for the Understanding of Occluded Objects with Self-Supervised Test-Time Learning
Shuxin Yang, Xinhan Di
Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles
Levi Cai, Kevin Chang, Yogesh Girdhar
PC-Planner: Physics-Constrained Self-Supervised Learning for Robust Neural Motion Planning with Shape-Aware Distance Function
Xujie Shen, Haocheng Peng, Zesong Yang, Juzhan Xu, Hujun Bao, Ruizhen Hu, Zhaopeng Cui
Learning to Ground Existentially Quantified Goals
Martin Funkquist, Simon Ståhlberg, Hector Geffner
DIG-FACE: De-biased Learning for Generalized Facial Expression Category Discovery
Tingzhang Luo, Yichao Liu, Yuanyuan Liu, Andi Zhang, Xin Wang, Yibing Zhan, Chang Tang, Leyuan Liu, Zhe Chen
Quantum delegated and federated learning via quantum homomorphic encryption
Weikang Li, Dong-Ling Deng
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning
Alicia Li, Nishanth Kumar, Tomás Lozano-Pérez, Leslie Kaelbling
Learning to Obstruct Few-Shot Image Classification over Restricted Classes
Amber Yijia Zheng, Chiao-An Yang, Raymond A. Yeh
Learning from Demonstration with Implicit Nonlinear Dynamics Models
Peter David Fagan, Subramanian Ramamoorthy
Learning from Pattern Completion: Self-supervised Controllable Generation
Zhiqiang Chen, Guofan Fan, Jinying Gao, Lei Ma, Bo Lei, Tiejun Huang, Shan Yu
Effects of AI Feedback on Learning, the Skill Gap, and Intellectual Diversity
Christoph Riedl, Eric Bogert
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
Akira Imakura, Tetsuya Sakurai
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang, Dian Chen, Sergio Casas, Raquel Urtasun
Learning to Love Edge Cases in Formative Math Assessment: Using the AMMORE Dataset and Chain-of-Thought Prompting to Improve Grading Accuracy
Owen Henkel, Hannah Horne-Robinson, Maria Dyshel, Nabil Ch, Baptiste Moreau-Pernet, Ralph Abood
Learning Occlusion-aware Decision-making from Agent Interaction via Active Perception
Jie Jia, Yiming Shu, Zhongxue Gan, Wenchao Ding