科研进展：修订间差异
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= '''<span style="color:#800000"><span style="font-size:smaller">研究成果</span></span>''' = | = '''<span style="color:#800000"><span style="font-size:smaller">研究成果</span></span>''' = | ||
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'''<span style="font-family:微软雅黑; font-size:medium">Selected Paper</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">2022</span></span></p> | <p style="text-align:center"><span style="font-family:Times New Roman,Times,serif"><span style="font-size:larger">2022</span></span></p> | ||
#<span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">Wu J., etc. A Bayesian Optimal Social Law Synthesizing Mechanism for Strategic Agents[J]. '''''JAAMAS'''''</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">, Accepted</span></span> | |||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Du Y., Luo H., Yang H., Jiang J. and etc. InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation. </span></span><span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">In '''''Proceedings of '''''</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;"> '''''ECML PKDD 2022''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Du Y., Luo H., Yang H., Jiang J. and etc. InCo: Intermediate Prototype Contrast for Unsupervised Domain Adaptation. </span></span><span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">In '''''Proceedings of '''''</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;"> '''''ECML PKDD 2022''''', Accepted</span></span> | ||
#<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Xu M. etc. NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural Networks. </span></span><span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">In '''''Proceedings of '''''</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;"> '''''APWeb-WAIM 2022''''', Accepted</span></span> | #<span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;">Xu M. etc. NED-GNN: Detecting and Dropping Noisy Edges in Graph Neural Networks. </span></span><span style="font-size:medium;"><span style="font-family:Times New Roman,Times,serif;">In '''''Proceedings of '''''</span></span><span style="font-family:Times New Roman,Times,serif;"><span style="font-size:medium;"> '''''APWeb-WAIM 2022''''', Accepted</span></span> | ||