Systems | Information | Learning | Optimization
 

Class Type: Spring 2025

SILO: Towards Principled AI-Agents with Decentralized and Asymmetric Information

Abstract: AI Models have been increasingly deployed to develop “Autonomous Agents” for decision-making, with prominent application examples including playing Go and video games, robotics, autonomous driving, healthcare, human-assistant, etc. Most such success stories naturally involve multiple AI-agents interacting dynamically with each other and humans. More importantly, these agents oftentimes operate with asymmetric …

SILO: Neural Operators for Scientific Applications: Learning on Function Spaces

Abstract: Applying AI to scientific problems like weather forecasting and aerodynamics is an active research area, promising to accelerate model development and enable faster scientific discovery and engineering design. In practice, these applications require learning spatiotemporal processes and solutions to partial differential equations on continuous domains at multiple scales – …

SILO: Self-Improving Transformers: Overcoming Length Generalization Challenges

Abstract: Large language models can perform algorithmic tasks through test-time computation but struggle to generalize far beyond the task difficulty of the training distribution. These limitations manifest across even simple tasks like arithmetic, string manipulation, and maze solving, where transformers learn shortcuts rather than the underlying algorithms. While prior solutions …