V
Vahab Mirrokni
Researcher at Google
Publications - 390
Citations - 16175
Vahab Mirrokni is an academic researcher from Google. The author has contributed to research in topics: Computer science & Common value auction. The author has an hindex of 57, co-authored 346 publications receiving 14255 citations. Previous affiliations of Vahab Mirrokni include Vassar College & Microsoft.
Papers
More filters
Posted Content
Randomized Composable Core-sets for Distributed Submodular Maximization
TL;DR: In this article, a randomized composable core-set for submodular maximization under a cardinality constraint is presented. But the problem is solved by partitioning the data into smaller pieces, solving the problem on each piece and finally obtaining a solution inside the union of the representative solutions for all pieces.
Book ChapterDOI
A Recommender System Based on Local Random Walks and Spectral Methods
Zeinab Abbassi,Vahab Mirrokni +1 more
TL;DR: This paper designs recommender systems for blogs based on the link structure among them and proposes algorithms based on refined random walks and spectral methods that perform very well for blogs, since the average degree of nodes in the blog graph is large.
Journal ArticleDOI
The landscape of the proximal point method for nonconvex–nonconcave minimax optimization
TL;DR: In this paper , the authors study the classic proximal point method (PPM) applied to nonconvex-nonconcave minimax problems and find that a classic generalization of the Moreau envelope by Attouch and Wets provides key insights.
Proceedings ArticleDOI
Towards Efficient Auctions in an Auto-bidding World
TL;DR: In this paper, a family of auctions with boosts is proposed to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints, and empirical results validate their theoretical findings and show that both the welfare and revenue can be improved by selecting the weight of the boosts properly.
Posted Content
Bid Optimization in Broad-Match Ad auctions
TL;DR: In this paper, an LP-based polynomial-time algorithm was proposed to find the optimal bidding strategy for the broad match problem in the query language model, where the advertiser can only bid on a subset of keywords as an exact or broad match.