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Events for January 10, 2024

  • Repeating EventEiS Communications Hub Drop-In Hours

    Wed, Jan 10, 2024 @ 10:00 AM - 01:00 PM

    Viterbi School of Engineering Student Affairs

    Workshops & Infosessions

    Viterbi Ph.D. students are invited to stop by the EiS Communications Hub for one-on-one instruction for their academic and professional communications tasks. All instruction is provided by Viterbi faculty at the Engineering in Society Program.

    Location: Ronald Tutor Hall of Engineering (RTH) - 222A

    Audiences: Viterbi Ph.D. Students

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    Contact: Helen Choi

    Event Link: https://sites.google.com/usc.edu/eishub/home?authuser=0

  • PhD Thesis Defense - Chung-Wei Lee

    Wed, Jan 10, 2024 @ 01:00 PM - 02:00 PM

    Thomas Lord Department of Computer Science

    University Calendar

    PhD Thesis Defense - Chung-Wei Lee
    Committee Members: 
    Haipeng Luo (chair)
    Ashutosh Nayyar
    Vatsal Sharan
    No-Regret Learning and Last-Iterate Convergence in Games
    No-regret learning (or online learning) is a general framework for studying sequential decision-making. Within this framework, the learner iteratively makes decisions, receives feedback, and adjusts their strategies. In this thesis, we consider analyzing the learning dynamics of no-regret algorithms in game scenarios where players play a single game repeatedly with particular no-regret algorithms. This exploration not only raises fundamental questions at the intersection of machine learning and game theory but also stands as a vital element when developing recent breakthroughs in artificial intelligence.
    A notable instance of this influence is the widespread adoption of the “self-play” concept in game AI development, exemplified in games such as Go and Poker. With this technique, AI agents learn how to play by competing against themselves to enhance their performance step by step. In the terminology of literature focused on learning in games, the method involves running a set of online learning algorithms for players in the game to compute and approximate their game equilibria. To learn more efficiently in games, it is critical to design better online learning algorithms. Standard notions evaluating online learning algorithms in games include “regret,” assessing the average quality of iterates, and “last-iterate convergence,” representing the quality of the final iterates.
    In this thesis, we design online learning algorithms and prove that they achieve near-optimal regret or fast last-iterate convergence in various game settings. We start from the simplest two-player zero-sum normal-form games and extend the results to multi-player games, extensive-form games that capture sequential interaction and imperfect information, and finally, the most general convex games. Moreover, we also analyze the weaknesses of prevalent online learning algorithms widely employed in practice and propose a fix for them. This not only makes the algorithms more robust but also sheds light on getting better learning algorithms for artificial intelligence in the future. 

    Location: Ronald Tutor Hall of Engineering (RTH) - 306

    Audiences: Everyone Is Invited

    Contact: CS Events