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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">This is an announcement of Jibang Wu’s MS Presentation<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">===============================================<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">Candidate: Jibang Wu<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">Date: September 22, 2023<span class="apple-converted-space"> </span><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">Time: 12pm-1pm<o:p></o:p></span></p>
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<p class="MsoNormal"><span class="s1"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:black">Remote Location: <a href="https://urldefense.com/v3/__https:/uchicago.zoom.us/j/95642982235?pwd=QzAyV01iTVVUd1N1OTlhQkYvU2FtZz09__;!!BpyFHLRN4TMTrA!94cnu3YSpTDZYQQIfaa92NaxCI7vRDVYBhcwU2us_c1VbV09AyffNpir-PQcgyLGFoXIxSlqlwdoMMG-iiUtpKco0-g$" title="https://urldefense.com/v3/__https://uchicago.zoom.us/j/95642982235?pwd=QzAyV01iTVVUd1N1OTlhQkYvU2FtZz09__;!!BpyFHLRN4TMTrA!94cnu3YSpTDZYQQIfaa92NaxCI7vRDVYBhcwU2us_c1VbV09AyffNpir-PQcgyLGFoXIxSlqlwdoMMG-iiUtpKco0-g$"><span style="color:#0563C1">https://uchicago.zoom.us/j/95642982235?pwd=QzAyV01iTVVUd1N1OTlhQkYvU2FtZz09</span></a></span></span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#DCA10D"><o:p></o:p></span></p>
<p class="MsoNormal"><span class="s1"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:black"> </span></span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#DCA10D"><o:p></o:p></span></p>
<p class="MsoNormal"><span class="s1"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:black">Location: JCL 267</span></span><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#DCA10D"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">M.S. Paper Title: Robust Stackelberg Equilibria <o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">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-ä 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.<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">Advisors: Haifeng Xu<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121"> <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Helvetica Neue";color:#212121">Committee Members: Haifeng Xu, Bo Li, Rad Niazadeh<o:p></o:p></span></p>
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