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

[Vassilis Tsotras]

Colleagues,
reminder for the AI Seminar tomorrow at noon; please use the link below 
to register if you plan to attend.

Sincerely,
V. Tsotras

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On 2025-02-03 10:19, Vassilis Tsotras wrote:
> 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.
> 
> ------------------------------------
> 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