Open Problem

Open problems in machine learning and related fields represent significant challenges hindering progress in various applications. Current research focuses on improving the capabilities of large language models (LLMs) in problem-solving, particularly under incomplete information, and developing tighter theoretical bounds for algorithms in areas like reinforcement learning and kernelized bandits. Addressing these open problems is crucial for advancing the theoretical understanding and practical applicability of machine learning across diverse domains, from energy networks to healthcare and cybersecurity.