#### Filter Results:

- Full text PDF available (22)

#### Publication Year

1991

2017

- This year (3)
- Last 5 years (12)
- Last 10 years (18)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Tamás Sarlós
- 2006 47th Annual IEEE Symposium on Foundations of…
- 2006

Several results appeared that show significant reduction in time for matrix multiplication, singular value decomposition as well as linear (lscr<sub>2</sub>) regression, all based on data dependent random sampling. Our key idea is that low dimensional embeddings can be used to eliminate data dependence and provide more versatile, linear time pass efficient… (More)

- Dániel Fogaras, Balázs Rácz, Károly Csalogány, Tamás Sarlós
- Internet Mathematics
- 2005

Personalized PageRank expresses link-based page quality around userselected pages in a similar way as PageRank expresses quality over the entire web. Existing personalized PageRank algorithms can, however, serve online queries only for a restricted choice of pages. In this paper we achieve full personalization by a novel algorithm that precomputes a compact… (More)

- András A. Benczúr, Károly Csalogány, Tamás Sarlós, Máté Uher
- AIRWeb
- 2005

Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed… (More)

- Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós
- Numerische Mathematik
- 2011

Least squares approximation is a technique to find an approximate solution to a system of linear equations that has no exact solution. In a typical setting, one lets n be the number of constraints and d be the number of variables, with n d. Then, existing exact methods find a solution vector in O(nd2) time. We present two randomized algorithms that provide… (More)

- Anirban Dasgupta, Ravi Kumar, Tamás Sarlós
- STOC
- 2010

Dimension reduction is a key algorithmic tool with many applications including nearest-neighbor search, compressed sensing and linear algebra in the streaming model. In this work we obtain a <i>sparse</i> version of the fundamental tool in dimension reduction -- the Johnson-Lindenstrauss transform. Using hashing and local densification, we construct a… (More)

- Benjamin Moseley, Anirban Dasgupta, Ravi Kumar, Tamás Sarlós
- SPAA
- 2011

The map-reduce paradigm is now standard in industry and academia for processing large-scale data. In this work, we formalize job scheduling in map-reduce as a novel generalization of the two-stage classical <i>flexible</i> flow shop (FFS) problem: instead of a single task at each stage, a job now consists of a set of tasks per stage. For this… (More)

- Quoc V. Le, Tamás Sarlós, Alexander J. Smola
- ICML
- 2013

User modeling on the Web has rested on the fundamental assumption of Markovian behavior --- a user's next action depends only on her current state, and not the history leading up to the current state. This forms the underpinning of PageRank web ranking, as well as a number of techniques for targeting advertising to users. In this work we examine the… (More)

Personalized PageRank expresses link-based page quality around user selected pages. The only previous personalized PageRank algorithm that can serve on-line queries for an unrestricted choice of pages on large graphs is our Monte Carlo algorithm [WAW 2004]. In this paper we achieve unrestricted personalization by combining rounding and randomized sketching… (More)

- Anirban Dasgupta, Ravi Kumar, Tamás Sarlós
- WWW
- 2014

Networks are characterized by nodes and edges. While there has been a spate of recent work on estimating the number of nodes in a network, the edge-estimation question appears to be largely unaddressed. In this work we consider the problem of estimating the average degree of a large network using efficient random sampling, where the number of nodes is not… (More)