Results 1  10
of
4,242
Quadratic Span Programs and Succinct NIZKs without PCPs
"... We introduce a new characterization of the NP complexity class, called Quadratic Span Programs (QSPs), which is a natural extension of span programs defined by Karchmer and Wigderson. Our main motivation is the construction of succinct arguments of NPstatements that are quick to construct and verif ..."
Abstract

Cited by 71 (8 self)
 Add to MetaCart
We introduce a new characterization of the NP complexity class, called Quadratic Span Programs (QSPs), which is a natural extension of span programs defined by Karchmer and Wigderson. Our main motivation is the construction of succinct arguments of NPstatements that are quick to construct
Square Span Programs with Applications to Succinct NIZK Arguments
"... Abstract. We propose a new characterization of NP using square span programs (SSPs). We first characterize NP as affine map constraints on small vectors. We then relate this characterization to SSPs, which are similar but simpler than Quadratic Span Programs (QSPs) and Quadratic Arithmetic Programs ..."
Abstract
 Add to MetaCart
Abstract. We propose a new characterization of NP using square span programs (SSPs). We first characterize NP as affine map constraints on small vectors. We then relate this characterization to SSPs, which are similar but simpler than Quadratic Span Programs (QSPs) and Quadratic Arithmetic Programs
An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
, 2008
"... ..."
Bayes Factors
, 1995
"... In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null ..."
Abstract

Cited by 1766 (74 self)
 Add to MetaCart
In a 1935 paper, and in his book Theory of Probability, Jeffreys developed a methodology for quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now called the Bayes factor, which is the posterior odds of the null hypothesis when the prior probability on the null is onehalf. Although there has been much discussion of Bayesian hypothesis testing in the context of criticism of P values, less attention has been given to the Bayes factor as a practical tool of applied statistics. In this paper we review and discuss the uses of Bayes factors in the context of five scientific applications in genetics, sports, ecology, sociology and psychology.
How to do xtabond2: An introduction to “Difference” and “System” GMM in Strata
 CENTER FOR GLOBAL DEVELOPMENT, WORKING PAPER NO. 103
, 2006
"... ..."
The Endogeneity of the Optimum Currency Area Criteria
 FISCAL POLICY IN LATIN AMERICA” NBER MACROECONOMICS ANNUAL
, 1998
"... A country’s suitability for entry into a currency union depends on a number of economic conditions. These include, inter alia, the intensity of trade with other potential members of the currency union, and the extent to which domestic business cycles are correlated with those of the other countries. ..."
Abstract

Cited by 421 (18 self)
 Add to MetaCart
A country’s suitability for entry into a currency union depends on a number of economic conditions. These include, inter alia, the intensity of trade with other potential members of the currency union, and the extent to which domestic business cycles are correlated with those of the other countries. But international trade patterns and international business cycle correlations are endogenous. This paper develops and investigates the relationship between the two phenomenon. Using thirty years of data for twenty industrialized countries, we uncover a strong and striking empirical finding: countries with closer trade links tend to have more tightly correlated business cycles. It follows that countries are more likely to satisfy the criteria for entry into a currency
Apprenticeship Learning via Inverse Reinforcement Learning
 In Proceedings of the Twentyfirst International Conference on Machine Learning
, 2004
"... We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be di#cul ..."
Abstract

Cited by 371 (11 self)
 Add to MetaCart
We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be di#cult to write down an explicit reward function specifying exactly how di#erent desiderata should be traded o#. We think of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and give an algorithm for learning the task demonstrated by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output by the algorithm will attain performance close to that of the expert, where here performance is measured with respect to the expert 's unknown reward function.
Results 1  10
of
4,242