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This is an announcement of Jibang Wu’s MS Presentation</div>
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Candidate: Jibang Wu</div>
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Date: September 22, 2023<span class="apple-converted-space"> </span></div>
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Time: 12pm-1pm</div>
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<span class="s1" style="color: black;">Remote Location: <a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/95642982235?pwd=QzAyV01iTVVUd1N1OTlhQkYvU2FtZz09__;!!BpyFHLRN4TMTrA!94cnu3YSpTDZYQQIfaa92NaxCI7vRDVYBhcwU2us_c1VbV09AyffNpir-PQcgyLGFoXIxSlqlwdoMMG-iiUtpKco0-g$" style="color: rgb(5, 99, 193);" class="">https://uchicago.zoom.us/j/95642982235?pwd=QzAyV01iTVVUd1N1OTlhQkYvU2FtZz09</a><o:p class=""></o:p></span></div>
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<span class="s1" style="color: black;">Location: JCL 267</span></div>
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M.S. Paper Title: Robust Stackelberg Equilibria </div>
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Abstract: This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equilibrium (SSE). The RSE accounts for any possible up-to-δ suboptimal follower
responses in Stackelberg games and is adopted to improve the robustness of the leader's strategy. While a few variants of robust Stackelberg equilibrium have been considered in previous literature, the RSE solution concept we consider is importantly different
-- in some sense, it relaxes previously studied robust Stackelberg strategies and is applicable to much broader sources of uncertainties. We provide a thorough investigation of several fundamental properties of RSE, including its utility guarantees, algorithmics,
and learnability. We first show that the RSE we defined always exists and thus is well-defined. Then we characterize how the leader's utility in RSE changes with the robustness level considered. On the algorithmic side, we show that, in sharp contrast to the
tractability of computing an SSE, it is NP-hard to obtain a fully polynomial approximation scheme (FPTAS) for any constant robustness level. Nevertheless, we develop a quasi-polynomial approximation scheme (QPTAS) for RSE. Finally, we examine the learnability
of the RSE in a natural learning scenario, where both players' utilities are not known in advance, and provide almost tight sample complexity results on learning the RSE. As a corollary of this result, we also obtain an algorithm for learning SSE, which strictly
improves a key result of Bai et al. in terms of both utility guarantee and computational efficiency.</div>
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Advisors: Haifeng Xu</div>
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Committee Members: Haifeng Xu, Bo Li, Rad Niazadeh</div>
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