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.
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Proceedings Article
Accelerating Gradient Boosting Machine
TL;DR: In this article, the authors proposed Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques into the design of GBM, which is the first GBM type of algorithm with theoretically-justified accelerated convergence rate.
Proceedings Article
Bi-Objective Online Matching and Submodular Allocations
TL;DR: The first results for bi-objective online sub modular optimization are given, providing almost matching upper and lower bounds for allocating items to agents with two submodular value functions.
Journal ArticleDOI
A relation between choosability and uniquely list colorability
TL;DR: If G is a connected non-regular multigraph with a list assignment L of edges such that for each edge e = uv, |Le| = max {d(u),d(v)}, then G is not uniquely L-colorable and it is conjecture that this result holds for any graph.
Posted Content
Massively Parallel Dynamic Programming on Trees
MohammadHossein Bateni,Soheil Behnezhad,Mahsa Derakhshan,MohammadTaghi Hajiaghayi,Vahab Mirrokni +4 more
TL;DR: This paper attempts to address the issue of dynamic programming in the Massively Parallel Computations (MPC) model which is a popular abstraction of MapReduce-like paradigms, and introduces two classes of graph problems that admit dynamic programming solutions on trees.
Posted Content
Dual Mirror Descent for Online Allocation Problems
TL;DR: In this paper, a general class of algorithms that achieve sub-linear expected regret compared to the hindsight optimal allocation are presented. But their regret is not independent of the revenue function and resource consumption of each request, which is unknown to the decision maker.