Machine Learning in Finance

Speaker: Xin Guo (UC Berkeley)

Date/Time: Wednesday, 11/5, 7:00pm CET (10:00am PST, 1:00pm EST)

Title: From LLM to RL and diffusion models, via (rough) differential equations

Abstract: Transfer learning is a machine learning technique that leverages knowledge acquired in one domain to enhance performance on a related task. It plays a central role in  the success of large language models (LLMs) such as GPT and BERT, which leverage pretraining to enable broad generalization across downstream applications. In this talk, I will discuss how reinforcement learning (RL), and in particular continuous time RL, can benefit from  transfer learning principles.  I will present convergence results formulated through stability analysis for stochastic control systems, using rough differential equation techniques. Finally, I will show how this analysis yields a natural corollary establishing robustness guarantees for a class of score-based generative diffusion models.

Based on joint work with Zijiu Lyu of UC Berkeley.