Reinforcement learning has been instrumental in many advances in AI, including medicine. In such applications, statements about the reliability of the results are necessary in addition to convergence results. Research in this direction is the topic of "Reinforcement Learning and its Application in Medicine and Large Language Models", a talk by Clemens Heitzinger of TU Wien. The talk is part of OFAI's 2023 Fall Lecture Series.
Members of the public are cordially invited to attend the talk in person (OFAI, Freyung 6/6/7, 1010 Vienna) or via Zoom on Wednesday, 31 January 2024 at 18:30 CET (UTC+1):
URL: https://us06web.zoom.us/j/84282442460?pwd=NHVhQnJXOVdZTWtNcWNRQllaQWFnQT09
Meeting ID: 842 8244 2460
Passcode: 678868
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Talk abstract: Reinforcement learning has been instrumental in many advances in AI in recent years. The most publicized is certainly the development of ChatGPT and large language models (LLM) in general; the last and crucial training step of ChatGPT is reinforcement learning with human feedback (RLHF). Still, in order to fully solve learning problems, statements about the reliability of the results are necessary in addition to convergence results. For example, reliability and trustworthiness of AI systems is of utmost importance in medicine and other safety critical areas. In this talk, reinforcement-learning algorithms for training LLM and for calculating optimal treatments of sepsis patients are described. The questions of convergence to an optimal policy and of reliability are addressed by PAC (probably approximately correct) estimates and other approaches to policy evaluation.
Speaker biography: Clemens Heitzinger is Co-Director of the Center for Artificial Intelligence and Machine Learning (CAIML) at TU Wien and Associate Professor in the Department of Computer Science (Informatics) at TU Wien. He received both his master's degree (Dipl.-Ing.) in applied mathematics and his PhD degree (Dr. techn.) in technical sciences with highest honors from TU Wien. He was a visiting researcher in the Department of Mathematics and Statistics at Arizona State University, a research associate in the School of Electrical and Computer Engineering at Purdue University, and a senior research associate in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at Cambridge University. In 2015, he returned to TU Wien as an associate professor. He is also Adjunct Professor in the School of Mathematical and Statistical Sciences at Arizona State University. He was awarded the START Prize, Austria's most prestigious award for young scientists, by the Austrian Science Fund (FWF) in 2013. He is author of Algorithms with Julia (Springer, 2022). His research interests are reinforcement learning and uncertainty quantification (in particular Bayesian inversion) with applications in the sciences, medicine, and engineering.
