Theoretical and algorithmic foundation of transfer learning
Ming Liang Ang (Ph.D. Student)
Transfer learning is key to adapting foundation models to new domains; however, its theoretical foundation remains underdeveloped. In my Ph.D. project with Carlo and Massi, our goal is to better understand the conditions under which transfer learning effectively occurs. By doing so, we hope to elucidate key design principles that can be applied to create improved transfer learning algorithms for foundation models. A pivotal aspect of our investigation is the transfer of knowledge across different domains, such as from natural language to tabular data. We aim to determine under which circumstances this is both possible and advantageous.
|Primary Host:||Carlo Ciliberto (University College London)|
|Exchange Host:||Massimiliano Pontil (Istituto Italiano di Tecnologia & University College London)|
|PhD Duration:||01 November 2023 - 01 November 2027|
|Exchange Duration:||01 January 2025 - 01 June 2025 - Ongoing|