General Strategy
Research on general strategies focuses on developing and optimizing methods for achieving specific goals across diverse domains, from mitigating online trolling to enhancing the efficiency of AI systems. Current efforts concentrate on leveraging human preferences to guide strategy selection, integrating sustainability considerations into AI development, and employing techniques like imitation learning and Bayesian optimization to improve model performance and efficiency. These advancements have significant implications for various fields, improving online community management, promoting responsible AI development, and accelerating scientific discovery through more efficient computational tools.
Papers
On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning
Arpit Guleria, J. Harshan, Ranjitha Prasad, B. N. Bharath
Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model
Harris Borman, Anna Leontjeva, Luiz Pizzato, Max Kun Jiang, Dan Jermyn