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- University of California, Santa BarbaraUS78 papers
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- University of Maryland, College ParkUS22 papers
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- Microsoft Research (United Kingdom)GB16 papers
- Princeton UniversityUS16 papers
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- Board of the Swiss Federal Institutes of TechnologyCH11 papers
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- University of California, RiversideUS9 papers
20 papers · 1 filter
You Share, I Share: Network Effects and Economic Incentives in P2P File-Sharing Systems
Mahyar Salek, Shahin Shayandeh, David Kempe
We study the interaction between network effects and external incentives on file sharing behavior in Peer-to-Peer (P2P) networks. Many current or envisioned P2P networks reward ind…
A Comprehensive Trainable Error Model for Sung Music Queries
W. P. Birmingham, C. J. Meek
We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typi…
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Andrew Cotter, Ohad Shamir, Nathan Srebro +1
Mini-batch algorithms have been proposed as a way to speed-up stochastic convex optimization problems. We study how such algorithms can be improved using accelerated gradient metho…
On the Locality of Codeword Symbols
Parikshit Gopalan, Cheng Huang, Huseyin Simitci +1
Consider a linear [n,k,d]_q code C. We say that that i-th coordinate of C has locality r, if the value at this coordinate can be recovered from accessing some other r coordinates o…
Using More Data to Speed-up Training Time
Shai Shalev-Shwartz, Ohad Shamir, Eran Tromer
In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently,…
Orthogonal Matching Pursuit with Replacement
Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon
In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this…