<div style="font-family: Arial, sans-serif; font-size: 14px;">Apologies for cross-posting.</div><div style="font-family: Arial, sans-serif; font-size: 14px;"><br></div><div style="font-family: Arial, sans-serif; font-size: 14px;">See below for call for papers. Submission deadline extended to <b>May 16.</b></div><div style="font-family: Arial, sans-serif; font-size: 14px;"><br></div><div style="font-family: Arial, sans-serif; font-size: 14px;">-------------------------</div><div style="font-family: Arial, sans-serif; font-size: 14px;"><b><br></b></div><div style="font-family: Arial, sans-serif; font-size: 14px;"><div><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">We are delighted to announce that the 2nd Workshop on Social Choice and Learning Algorithms (SCaLA-25) will take place at IJCAI in August of 2025 in Montreal, Canada. It will feature technical sessions, a keynote speaker, and opportunities to develop collaborations between researchers working in social choice and those working in machine learning.</span></p><br><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">The website and submission instructions can be found at the following link: <a href="https://sites.google.com/view/scala25" target="_blank" rel="noreferrer nofollow noopener">https://sites.google.com/view/scala25</a> </span></p><br><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">The <b>submission deadline is <strike>May 9</strike> <u>May 16</u>, 2025</b>. We encourage submissions of fully developed research projects as well as preliminary explorations of novel ideas. Submissions should include components from both fields of social choice and machine learning (or closely related topics).</span></p><br><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;font-weight:700;text-decoration:none;white-space:pre-wrap">Topics of interest include:</span></p><ul style="margin-top:0;margin-bottom:0;padding-inline-start:48px"><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Computational social choice</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Fair Division</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Matching</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Voting theory</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Sortition</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Clustering</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Ensemble learning</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Explainable ML</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Language models</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Preference learning</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">PAC learning</span></p></li></ul><br><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">Examples of interesting connections between these topics include, but are not at all limited to:</span></p><ul style="margin-top:0;margin-bottom:0;padding-inline-start:48px"><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Using machine learning to learn new mechanisms for matching</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Novel uses of social choice for ensemble learning</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Exploring the application of fair division concepts to clustering problems, or vice-versa</span></p></li><li style="font-size:11pt;text-decoration:none;white-space:pre" dir="ltr"><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="text-decoration:none;white-space:pre-wrap">Applying multi-winner voting concepts to multi-class classification tasks</span></p></li></ul></div><div><br></div><div>Please feel free to contact any member of the organizing committee with questions!<br><br><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;font-weight:700;text-decoration:none;white-space:pre-wrap">Organizing Committee</span></p><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">Ben Armstrong</span></p><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">Roy Fairstein</span></p><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">Nick Mattei</span></p><p style="line-height:1.38;margin-top:0pt;margin-bottom:0pt" dir="ltr"><span style="font-size:11pt;text-decoration:none;white-space:pre-wrap">Zoi Terzopoulou</span></p></div><br></div><div class="protonmail_signature_block protonmail_signature_block-empty" style="font-family: Arial, sans-serif; font-size: 14px;">
    <div class="protonmail_signature_block-user protonmail_signature_block-empty"></div>
    
            <div class="protonmail_signature_block-proton protonmail_signature_block-empty">
        
            </div>
</div>