<div dir="ltr"><div class="gmail-group-header" style="box-sizing:inherit;font-family:Mallory,Verdana,Arial,Helvetica,sans-serif;font-size:17px"><span class="gmail-field gmail-field-name-title gmail-field-type-ds gmail-field-label-hidden" style="box-sizing:inherit"><span style="box-sizing:inherit"><span class="gmail-odd gmail-first gmail-last" style="box-sizing:inherit"><h1 class="gmail-title" style="box-sizing:inherit;font-weight:300;padding:0px;font-feature-settings:"kern","liga","dlig";font-size:1.76471em;line-height:normal;font-stretch:normal;color:rgb(0,60,118);text-transform:uppercase;display:inline-block">OMAR MONTASSER</h1></span></span></span>, <span class="gmail-field gmail-field-name-field-university gmail-field-type-text gmail-field-label-hidden" style="box-sizing:inherit;margin-left:5px"><span style="box-sizing:inherit"><span class="gmail-odd gmail-first gmail-last" style="box-sizing:inherit">Toyota Technological Institute at Chicago</span></span></span><div class="gmail-field gmail-field-name-field-abstract-title gmail-field-type-text gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit;font-size:20px;font-weight:600;line-height:1.2;margin-bottom:1em;margin-top:0.5em">Title: What, When, and How can we Learn Adversarially Robustly?</div></div></div></div><div class="gmail-group-left" style="box-sizing:inherit;float:left;width:auto;padding-right:15.3125px;max-width:30%;font-family:Mallory,Verdana,Arial,Helvetica,sans-serif;font-size:17px"><div class="gmail-field gmail-field-name-field-image gmail-field-type-image gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit"><img src="https://statistics.yale.edu/sites/default/files/styles/user_picture_node/public/pic_0.jpg?itok=qblKQq3E" width="400" height="480" alt="" style="box-sizing: inherit; border: 0px; max-width: 100%; height: auto; vertical-align: bottom;"></div></div></div></div><div class="gmail-group-right" style="box-sizing:inherit;float:left;width:auto;max-width:65%;padding-left:22.9766px;font-family:Mallory,Verdana,Arial,Helvetica,sans-serif;font-size:17px"><div class="gmail-field gmail-field-name-field-event-time gmail-field-type-datetime gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit;color:rgb(0,60,118);font-size:18px;line-height:1.4"><span class="gmail-date-display-single" style="box-sizing:inherit">Wednesday, February 08, 2023<span class="gmail-date-display-range" style="box-sizing:inherit;float:left;width:427.492px"><span class="gmail-date-display-start" style="box-sizing:inherit">4:00PM</span> to <span class="gmail-date-display-end" style="box-sizing:inherit">5:00PM</span></span></span></div></div></div><div class="gmail-field gmail-field-name-field-location gmail-field-type-location gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit"><div class="gmail-location gmail-vcard" style="box-sizing:inherit"><div class="gmail-adr" style="box-sizing:inherit"><span class="gmail-fn" style="box-sizing:inherit">Mason Lab 211</span> <span class="gmail-map-icon" style="box-sizing:inherit;margin-left:0.25em;font-size:0.925em;line-height:1.55;letter-spacing:0.05em;word-spacing:0.05em;text-transform:lowercase;font-feature-settings:"smcp""><a href="http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us" style="box-sizing:inherit;outline:none;line-height:inherit;color:rgb(40,109,192)">see map</a> </span><div class="gmail-street-address" style="box-sizing:inherit">9 Hillhouse Ave</div><span class="gmail-locality" style="box-sizing:inherit">New Haven</span>, <span class="gmail-region" style="box-sizing:inherit">CT</span> <span class="gmail-postal-code" style="box-sizing:inherit">06511</span></div></div></div></div></div><div class="gmail-field gmail-field-name-field-website gmail-field-type-link-field gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit"><a href="https://home.ttic.edu/~omar/" style="box-sizing:inherit;text-decoration-line:none;outline:none;line-height:1.5;color:rgb(0,60,118);font-size:16px">Website</a></div></div></div></div><div class="gmail-group-footer" style="box-sizing:inherit;clear:both;padding-top:15px;font-family:Mallory,Verdana,Arial,Helvetica,sans-serif;font-size:17px"><div class="gmail-field gmail-field-name-body gmail-field-type-text-with-summary gmail-field-label-above" style="box-sizing:inherit"><div class="gmail-field-label" style="box-sizing:inherit;font-weight:bold">Information and Abstract: </div><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit"><p style="box-sizing:inherit;margin:0px 0px 1em;padding:0px">Despite extraordinary progress, current machine learning systems have been shown to be brittle against adversarial examples: seemingly innocuous but carefully crafted perturbations of test examples that cause machine learning predictors to misclassify. Can we learn predictors robust to adversarial examples? and how? There has been much empirical interest in this major challenge in machine learning, and in this talk, we will present a theoretical perspective. We will illustrate the need to go beyond traditional approaches and principles, such as empirical (robust) risk minimization, and present new algorithmic ideas with stronger robust learning guarantees.</p><p style="box-sizing:inherit;margin:0px 0px 1em;padding:0px">Bio:</p><p style="box-sizing:inherit;margin:0px 0px 1em;padding:0px">Omar Montasser is a PhD candidate at TTI-Chicago advised by Nathan Srebro. His research broadly explores the theory and foundations of machine learning. Recently, his research has focused on understanding and characterizing adversarially robust learning, and on designing learning algorithms with provable robustness guarantees under different settings. His work has been recognized by a best student paper award at COLT (2019).</p></div></div></div><div class="gmail-field gmail-field-name-field-event-description gmail-field-type-text-with-summary gmail-field-label-hidden" style="box-sizing:inherit"><div class="gmail-field-items" style="box-sizing:inherit"><div class="gmail-field-item even" style="box-sizing:inherit"><p style="box-sizing:inherit;margin:0px 0px 1em;padding:0px"><em style="box-sizing:inherit"><strong style="box-sizing:inherit">In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (</strong></em><a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa" rel="nofollow" style="box-sizing:inherit;outline:none;line-height:inherit;color:rgb(40,109,192);word-break:break-word">https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</a>)</p><p style="box-sizing:inherit;margin:0px 0px 1em;padding:0px"><em style="box-sizing:inherit"><strong style="box-sizing:inherit"><a href="https://0.0.0.10/" rel="nofollow" style="box-sizing:inherit;outline:none;line-height:inherit;color:rgb(40,109,192);word-break:break-word">3:30pm</a> -   Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue </strong></em></p></div></div></div></div></div>