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William He is a PhD student in the Computer Science Department Carnegie Mellon University, advised by Ryan O'Donnell. His research interests lie in the design and analysis of algorithms for quantum and classical statistics.
This 4-day workshop will mark the journey of learning theory from the fringes of TCS to a core topic distinguished by its many mutually rewarding interactions with other areas. It will highlight recent developments and current challenges including emerging...
Not long ago, two reasonable assumptions about machine learning were: (1) the primary mechanism to achieve learning is to tune parameters, and (2) because we have little prior knowledge to provide a strong inductive bias, learning must rely on big data and sophisticated statistics. Today, both assumptions seem out of date when one considers architecting learning agents that employ LLMs as subroutines. We will explore this new style of LLM-based learning agents, as well as theoretical questions they raise.