Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Mahdian, Mohammad"'
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 price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as $0$. For comparison, the price of anarchy of running VCG is $1
Externí odkaz:
http://arxiv.org/abs/2310.03105
Autor:
Huang, Yangsibo, Jiang, Haotian, Liu, Daogao, Mahdian, Mohammad, Mao, Jieming, Mirrokni, Vahab
In this paper, we study the setting in which data owners train machine learning models collaboratively under a privacy notion called joint differential privacy [Kearns et al., 2018]. In this setting, the model trained for each data owner $j$ uses $j$
Externí odkaz:
http://arxiv.org/abs/2305.15723
Hierarchical Clustering is a popular unsupervised machine learning method with decades of history and numerous applications. We initiate the study of differentially private approximation algorithms for hierarchical clustering under the rigorous frame
Externí odkaz:
http://arxiv.org/abs/2302.00037
In a typical optimization problem, the task is to pick one of a number of options with the lowest cost or the highest value. In practice, these cost/value quantities often come through processes such as measurement or machine learning, which are nois
Externí odkaz:
http://arxiv.org/abs/2207.09435
Streaming computation plays an important role in large-scale data analysis. The sliding window model is a model of streaming computation which also captures the recency of the data. In this model, data arrives one item at a time, but only the latest
Externí odkaz:
http://arxiv.org/abs/2110.15533
In this paper, we study the $r$-gather problem, a natural formulation of minimum-size clustering in metric spaces. The goal of $r$-gather is to partition $n$ points into clusters such that each cluster has size at least $r$, and the maximum radius of
Externí odkaz:
http://arxiv.org/abs/2106.02685
In this paper, we study a number of well-known combinatorial optimization problems that fit in the following paradigm: the input is a collection of (potentially inconsistent) local relationships between the elements of a ground set (e.g., pairwise co
Externí odkaz:
http://arxiv.org/abs/2102.11548
Autor:
Vainstein, Danny, Chatziafratis, Vaggos, Citovsky, Gui, Rajagopalan, Anand, Mahdian, Mohammad, Azar, Yossi
Recently, Hierarchical Clustering (HC) has been considered through the lens of optimization. In particular, two maximization objectives have been defined. Moseley and Wang defined the \emph{Revenue} objective to handle similarity information given by
Externí odkaz:
http://arxiv.org/abs/2101.10639
A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input. Our goal is to identify the optima
Externí odkaz:
http://arxiv.org/abs/2010.11450