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  • Optimal methods for reinforcement learning: Efficient algorithms with instance-dependent guarantees | Wenlong Mou

    Special Seminar Series
    SDSC, The Auditorium 9836 Hopkins Dr, La Jolla, San Diego, CA, United States

    Reinforcement learning (RL) is a pillar for modern artificial intelligence. Compared to classical statistical learning, several new statistical and computational phenomena arise from RL problems, leading to different trade-offs in the choice of the estimators, tuning of their parameters, and the design of efficient algorithms. In many settings, asymptotic and/or worst-case theory fails to provide the relevant guidance.
    In this talk, I present recent advances that involve a more refined approach to RL, one that leads to non-asymptotic and instance-optimal guarantees. The bulk of this talk focuses on function approximation methods for policy evaluation. I establish a novel class of optimal and instance-dependent oracle inequalities for projected Bellman equations, as well as efficient computational algorithms achieving them. Among other results, I will highlight how the instance-optimal guarantees guide the selection of tuning parameters in temporal different methods, and tackle the instability issue with general function classes. Drawing on this perspective, I will also discuss a novel class of stochastic approximation methods that yield optimal statistical guarantees for policy optimization problems.

  • Scientific Machine Learning Symposium

    The Great Hall 9500 Gilman Drive, La Jolla, CA, United States

    Recent progress in Artificial Intelligence (AI) and Machine Learning (ML) has provided groundbreaking methods for processing large data sets. These new techniques are particularly powerful when dealing with scientific data with complex structures, non-linear relationships, and unknown uncertainties that are challenging to model and analyze with traditional tools. This has triggered a flurry of activity in science and engineering, developing new methods to tackle problems which used to be impossible or extremely hard to deal with.

    The goal of this symposium is to bring together researchers and practitioners at the intersection of AI and Science, to discuss opportunities to use AI to accelerate scientific discovery, and to explore the potential of scientific knowledge to guide AI development. The symposium will provide a platform to nurture the research community, to fertilize interdisciplinary ideas, and shape the vision of future developments in the rapidly growing field of AI + Science.

    We plan to use the symposium as the launching event for the AI + Science event series, co-hosted by Computer Science and Engineering(CSE), Halıcıoğlu Data Science Institute (HDSI), and Scripps Institution of Cceanography(SIO) at UC San Diego. The symposium will include a combination of invited talks, posters, panel discussions, social and networking events. The first event will put a particular emphasis on AI + physical sciences. We will invite contribution and participation from physics, engineering, and oceanography, among others. Part of the program will highlight the research from climate science, as a result of our DOE funded scientific ML project for tackling climate extremes.

  • Structured Transformer Models for NLP

    Colloquia Lecture Series
    SDSC, The Synthesis Center 9500 Gilman Drive, La Jolla, CA, United States

    The field of natural language processing has recently unlocked a wide range of new capabilities through the use of large language models, such as GPT-4. The growing application of these models motivates developing a more thorough understanding of how and why they work, as well as further improvements in both quality and efficiency.

    In this talk, I will present my work on analyzing and improving the Transformer architecture underlying today's language models through the study of how information is routed between multiple words in an input. I will show that such models can predict the syntactic structure of text in a variety of languages, and discuss how syntax can inform our understanding of how the networks operate. I will also present my work on structuring information flow to build radically more efficient models, including models that can process text of up to one million words, which enables new possibilities for NLP with book-length text.