[AI Seminar Series] Seminar by Prof. Yinglun Zhu, Friday February 7th, 12-1pm, MRB Seminar Room

[Vassilis Tsotras]

The next talk at the AI Seminar Series will be next Friday, February 
7th, 12:00-1:00pm at the MRB Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-1233138132349

The talk will be given by Prof. Yinglun Zhu, Department of Electrical 
and Computer Engineering, UCR

TITLE:
"Efficient Sequential Decision Making with Large Language Models"

ABSTRACT:
This presentation focuses on extending the success of large language 
models (LLMs) to sequential decision making. Existing efforts either (i) 
re-train or finetune LLMs for decision making, or (ii) design prompts 
for pretrained LLMs. The former approach suffers from the computational 
burden of gradient updates, and the latter approach does not show 
promising results. In this presentation, I'll talk about a new approach 
that leverages online model selection algorithms to efficiently 
incorporate LLMs agents into sequential decision making. Statistically, 
our approach significantly outperforms both traditional decision making 
algorithms and vanilla LLM agents. Computationally, our approach avoids 
the need for expensive gradient updates of LLMs, and throughout the 
decision making process, it requires only a small number of LLM calls. 
We conduct extensive experiments to verify the effectiveness of our 
proposed approach. As an example, on a large-scale Amazon dataset, our 
approach achieves more than a 6x performance gain over baselines while 
calling LLMs in only 1.5% of the time steps.

Bio: Yinglun Zhu is an assistant professor in the ECE department at the 
University of California, Riverside; he is also affiliated with the CSE 
department, the RAISE at UCR Institute, and the Center for Robotics and 
Intelligent Systems. Yinglun's research interest is in interactive 
machine learning, which includes learning paradigms such as active 
learning, bandits, and reinforcement learning. Recently, Yinglun focuses 
on connecting interactive machine learning to large AI models (e.g., 
LLMs), from both algorithmic and systemic perspectives. Yinglun's 
research has been integrated into leading machine learning libraries and 
commercial products.

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Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents 
speakers working on cutting edge Foundational AI or apply AI in their 
research. The goal of these seminars is to inform the UCR community 
about current trends in AI research and promote collaborations between 
faculty in this emerging field.
These seminars are open to interested faculty and graduate students. 
Please forward this email to other colleagues or graduate students in 
your lab that may be interested. After the seminar a discussion will 
follow for questions, open problems, ideas for possible collaborations 
etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute