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SIGECOM
2010
ACM
149views ECommerce» more  SIGECOM 2010»
14 years 9 days ago
A new understanding of prediction markets via no-regret learning
We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We first show that any c...
Yiling Chen, Jennifer Wortman Vaughan
MFCS
2010
Springer
13 years 5 months ago
Weights of Exact Threshold Functions
We consider Boolean exact threshold functions defined by linear equations, and in general degree d polynomials. We give upper and lower bounds on the maximum magnitude (absolute va...
László Babai, Kristoffer Arnsfelt Ha...
STOC
2005
ACM
138views Algorithms» more  STOC 2005»
14 years 1 months ago
A new strategy for querying priced information
This paper focuses on competitive function evaluation in the context of computing with priced information. A function f is given together with a cost cx for each variable x of f. ...
Ferdinando Cicalese, Eduardo Sany Laber
COLT
2000
Springer
13 years 11 months ago
Entropy Numbers of Linear Function Classes
This paper collects together a miscellany of results originally motivated by the analysis of the generalization performance of the “maximum-margin” algorithm due to Vapnik and...
Robert C. Williamson, Alex J. Smola, Bernhard Sch&...
COCO
2010
Springer
149views Algorithms» more  COCO 2010»
13 years 10 months ago
The Gaussian Surface Area and Noise Sensitivity of Degree-d Polynomial Threshold Functions
Abstract. We prove asymptotically optimal bounds on the Gaussian noise sensitivity of degree-d polynomial threshold functions. These bounds translate into optimal bounds on the Gau...
Daniel M. Kane