Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Zhu, Wennan"'
Autor:
Aggarwal, Gagan, Badanidiyuru, Ashwinkumar, Balseiro, Santiago R., Bhawalkar, Kshipra, Deng, Yuan, Feng, Zhe, Goel, Gagan, Liaw, Christopher, Lu, Haihao, Mahdian, Mohammad, Mao, Jieming, Mehta, Aranyak, Mirrokni, Vahab, Leme, Renato Paes, Perlroth, Andres, Piliouras, Georgios, Schneider, Jon, Schvartzman, Ariel, Sivan, Balasubramanian, Spendlove, Kelly, Teng, Yifeng, Wang, Di, Zhang, Hanrui, Zhao, Mingfei, Zhu, Wennan, Zuo, Song
In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opportunities that have arisen as markets embrace this autobidding and co
Externí odkaz:
http://arxiv.org/abs/2408.07685
We study the efficiency of non-truthful auctions for auto-bidders with both return on spend (ROS) and budget constraints. The efficiency of a mechanism is measured by the price of anarchy (PoA), which is the worst case ratio between the liquid welfar
Externí odkaz:
http://arxiv.org/abs/2310.09271
In federated frequency estimation (FFE), multiple clients work together to estimate the frequencies of their collective data by communicating with a server that respects the privacy constraints of Secure Summation (SecSum), a cryptographic multi-part
Externí odkaz:
http://arxiv.org/abs/2306.09396
We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the heterogeneous scenario where the quantity of data can be different for ea
Externí odkaz:
http://arxiv.org/abs/2206.03008
Autor:
Wang, Jianyu, Charles, Zachary, Xu, Zheng, Joshi, Gauri, McMahan, H. Brendan, Arcas, Blaise Aguera y, Al-Shedivat, Maruan, Andrew, Galen, Avestimehr, Salman, Daly, Katharine, Data, Deepesh, Diggavi, Suhas, Eichner, Hubert, Gadhikar, Advait, Garrett, Zachary, Girgis, Antonious M., Hanzely, Filip, Hard, Andrew, He, Chaoyang, Horvath, Samuel, Huo, Zhouyuan, Ingerman, Alex, Jaggi, Martin, Javidi, Tara, Kairouz, Peter, Kale, Satyen, Karimireddy, Sai Praneeth, Konecny, Jakub, Koyejo, Sanmi, Li, Tian, Liu, Luyang, Mohri, Mehryar, Qi, Hang, Reddi, Sashank J., Richtarik, Peter, Singhal, Karan, Smith, Virginia, Soltanolkotabi, Mahdi, Song, Weikang, Suresh, Ananda Theertha, Stich, Sebastian U., Talwalkar, Ameet, Wang, Hongyi, Woodworth, Blake, Wu, Shanshan, Yu, Felix X., Yuan, Honglin, Zaheer, Manzil, Zhang, Mi, Zhang, Tong, Zheng, Chunxiang, Zhu, Chen, Zhu, Wennan
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection. The distributed learning process can be formulated as solving f
Externí odkaz:
http://arxiv.org/abs/2107.06917
Forming better stable solutions in Group Formation Games inspired by Internet Exchange Points (IXPs)
Autor:
Anshelevich, Elliot, Zhu, Wennan
We study a coordination game motivated by the formation of Internet Exchange Points (IXPs), in which agents choose which facilities to join. Joining the same facility as other agents you communicate with has benefits, but different facilities have di
Externí odkaz:
http://arxiv.org/abs/2008.12235
We develop new voting mechanisms for the case when voters and candidates are located in an arbitrary unknown metric space, and the goal is to choose a candidate minimizing social cost: the total distance from the voters to this candidate. Previous wo
Externí odkaz:
http://arxiv.org/abs/1906.10562
The discovery of heavy hitters (most frequent items) in user-generated data streams drives improvements in the app and web ecosystems, but can incur substantial privacy risks if not done with care. To address these risks, we propose a distributed and
Externí odkaz:
http://arxiv.org/abs/1902.08534
Autor:
Anshelevich, Elliot, Zhu, Wennan
In this work we consider general facility location and social choice problems, in which sets of agents $\mathcal{A}$ and facilities $\mathcal{F}$ are located in a metric space, and our goal is to assign agents to facilities (as well as choose which f
Externí odkaz:
http://arxiv.org/abs/1805.03103
Autor:
Anshelevich, Elliot, Zhu, Wennan
We study ordinal approximation algorithms for maximum-weight bipartite matchings. Such algorithms only know the ordinal preferences of the agents/nodes in the graph for their preferred matches, but must compete with fully omniscient algorithms which
Externí odkaz:
http://arxiv.org/abs/1707.01608