Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Zhihao Gavin Tang"'
Autor:
ZHIHAO GAVIN TANG1 tang.zhihao@mail.shufe.edu.cn, XIAOWEI WU2 xiaoweiwu@um.edu.mo, YUHAO ZHANG3 zhang_yuhao@sjtu.edu.cn
Publikováno v:
Journal of the ACM. Dec2023, Vol. 70 Issue 6, p1-32. 32p.
Publikováno v:
Theoretical Computer Science. 928:13-26
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1c070dc14173abbcabd2362256f91b1b
https://doi.org/10.1016/j.tcs.2022.06.007
https://doi.org/10.1016/j.tcs.2022.06.007
Publikováno v:
Proceedings of the ACM Web Conference 2023.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5a754d55085d6542da6618275c1bd76
https://doi.org/10.1145/3543507.3583428
https://doi.org/10.1145/3543507.3583428
Publikováno v:
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977554
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::39f35d8470cbda6617d9dbfe7faeb8a7
https://doi.org/10.1137/1.9781611977554.ch145
https://doi.org/10.1137/1.9781611977554.ch145
Publikováno v:
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977554
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d105e5ae414a7ff8c2af71ee1456d882
https://doi.org/10.1137/1.9781611977554.ch147
https://doi.org/10.1137/1.9781611977554.ch147
Publikováno v:
Journal of the ACM; May2020, Vol. 67 Issue 3, p1-25, 25p
Publikováno v:
Journal of the ACM. 67:1-25
We introduce a fully online model of maximum cardinality matching in which all vertices arrive online. On the arrival of a vertex, its incident edges to previously arrived vertices are revealed. Each vertex has a deadline that is after all its neighb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da8089840e24dd7d8e3a135be969f8a4
https://doi.org/10.1145/3390890
https://doi.org/10.1145/3390890
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:506-518
We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optimal solutions, and consider confiden
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::644d463b9032252df0d6df73d07526cc
https://doi.org/10.1109/tkde.2018.2880448
https://doi.org/10.1109/tkde.2018.2880448
Publikováno v:
ACM SIGecom Exchanges. 17:54-61
Consider a fundamental problem in microeconomics: selling a single item to a number of potential buyers, who independently draw their values from regular and publicly known distributions. There are four mechanisms widely studied in the literature and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::552df9633f14ff5133e88f74b17cd220
https://doi.org/10.1145/3381329.3381335
https://doi.org/10.1145/3381329.3381335