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Structure and Learning of Valuation Functions
"... We discuss structural results and learning algorithms for submodular and fractionally subadditive valuation functions. While learning these valuation functions over general distributions turns out to be hard, we present compact approximate representations and efficient learning algorithms for such f ..."
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We discuss structural results and learning algorithms for submodular and fractionally subadditive valuation functions. While learning these valuation functions over general distributions turns out to be hard, we present compact approximate representations and efficient learning algorithms
Learning Valuation Functions
 25TH ANNUAL CONFERENCE ON LEARNING THEORY
, 2012
"... A core element of microeconomics and game theory is that consumers have valuation functions over bundles of goods and that these valuations functions drive their purchases. A common assumption is that these functions are subadditive meaning that the value given to a bundle is at most the sum of valu ..."
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Cited by 17 (2 self)
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A core element of microeconomics and game theory is that consumers have valuation functions over bundles of goods and that these valuations functions drive their purchases. A common assumption is that these functions are subadditive meaning that the value given to a bundle is at most the sum
Sketching Valuation Functions
, 2011
"... Motivated by the problem of querying and communicating bidders ’ valuations in combinatorial auctions, we study how well different classes of set functions can be sketched. More formally, let f be a function mapping subsets of some ground set [n] to the nonnegative real numbers. We say that f ′ is ..."
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Cited by 23 (2 self)
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Motivated by the problem of querying and communicating bidders ’ valuations in combinatorial auctions, we study how well different classes of set functions can be sketched. More formally, let f be a function mapping subsets of some ground set [n] to the nonnegative real numbers. We say that f
Bundle Selling by Online Estimation of Valuation Functions
"... Weconsidertheproblemofonlineselectionof a bundle of items when the cost of each item changes arbitrarily from round to round and the valuation function is initially unknown and revealed only through the noisy values of selected bundles (the bandit feedback setting). We are interested in learning sch ..."
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Cited by 1 (0 self)
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Weconsidertheproblemofonlineselectionof a bundle of items when the cost of each item changes arbitrarily from round to round and the valuation function is initially unknown and revealed only through the noisy values of selected bundles (the bandit feedback setting). We are interested in learning
The irreducibility of the space of curves of given genus
 Publ. Math. IHES
, 1969
"... Fix an algebraically closed field k. Let Mg be the moduli space of curves of genus g over k. The main result of this note is that Mg is irreducible for every k. Of course, whether or not M s is irreducible depends only on the characteristic of k. When the characteristic s o, we can assume that k ~ ..."
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Cited by 506 (2 self)
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strengthened his method so that it applies in all characteristics (SGA 7, ~968) 9 Mumford has also given a proof using theta functions in char. ~2. The result is this: Stable Reduction Theorem. Let R be a discrete valuation ring with quotient field K. Let A be an abelian variety over K. Then there exists a
VALUATION FUNCTIONALS AND STATIC NO ARBITRAGE OPTION PRICING FORMULAS
"... ABSTRACT. Often in practice, the implied volatility of an option is calculated to find the option price tomorrow or the prices of ‘nearby ’ options. To show that one does not need to adhere to the Black Scholes formula in this scheme, Figlewski has provided a new pricing formula and has shown that ..."
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that his ‘alternating passive model ’ performs as well as the BlackScholes formula [8]. The Figlewski model was modified by Henderson et al. so that the formula would have no static arbitrage [10]. In this paper, we show how to construct a huge class of such static no arbitrage pricing functions, making
Varying the Valuating Function and the Presentable Bank in Computerized Adaptive Testing
, 2011
"... In computerized adaptive testing, the most commonly used valuating function is the Fisher information function. When the goal is to keep item bank security at a maximum, the valuating function that seems most convenient is the matching criterion, valuating the distance between the estimated trait l ..."
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In computerized adaptive testing, the most commonly used valuating function is the Fisher information function. When the goal is to keep item bank security at a maximum, the valuating function that seems most convenient is the matching criterion, valuating the distance between the estimated trait
Results 1  10
of
1,729