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科研进展

添加50,239字节, 2024年4月13日 (星期六)

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<h1><strong><span style="color:#800000"><span style="font-size:smaller">研究成果</span></span></strong></h1>

= '''<hr /><p><strongspan style="color:#800000"><span style="font-family:微软雅黑; font-size:mediumsmaller">Selected Paper研究成果</span></strong></pspan>''' =

<p style="text-align:center"><span style="font-family:Times New Roman,Times,serif"><span style="font-size:larger">2017</span></span></p>-

<ol> <li><span style="font-family:Times New Roman,Times,serif"><span style="font-size:larger"><span style="color:black">Peng Y, Chen G, Zhai C, Wang C, Xie J. Multi-Label Learning by Exploiting label Correlations with LDA. In <em>Proceedings of ICTAI-17</em>. Boston, MA. USA. [Acceptted].</span></'''<span style="font-family:微软雅黑; font-size:medium">Selected Paper</span>'''<p style="text-align:center"><span style="font-family:Times New Roman,Times,serif"><span style="font-size:larger">2024</span></span></p> #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Zhang J., etc. &nbsp;Shapley-Optimized Reinforcement Learning for Human-Machine Collaboration Policy. In&nbsp;'''''Proceedings of&nbsp;'''''<b>''DASFAA 2024''</b>,&nbsp;Accepted</span></span> #<span style="font-

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