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Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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the true marginal at each leaf is approximately (0.1, 0.9), i.e., the leaf is 1 with high probability. We then generated untypical evidence at the leaves by sampling from the uniform distribution, (0.5, 0.5), or from the skewed distribu tion (0.9, 0. 1). We found that loopy propagation still converged2
Empirical margin distributions and bounding the generalization error of combined classifiers
 Ann. Statist
, 2002
"... Dedicated to A.V. Skorohod on his seventieth birthday We prove new probabilistic upper bounds on generalization error of complex classifiers that are combinations of simple classifiers. Such combinations could be implemented by neural networks or by voting methods of combining the classifiers, such ..."
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Cited by 158 (11 self)
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distribution to the true margin distribution uniformly over the classes of classifiers and prove the optimality of these rates.
Cryptanalysis of block ciphers with overdefined systems of equations
, 2002
"... Abstract. Several recently proposed ciphers, for example Rijndael and Serpent, are built with layers of small Sboxes interconnected by linear keydependent layers. Their security relies on the fact, that the classical methods of cryptanalysis (e.g. linear or differential attacks) are based on proba ..."
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Cited by 253 (22 self)
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on probabilistic characteristics, which makes their security grow exponentially with the number of rounds Nr. In this paper we study the security of such ciphers under an additional hypothesis: the Sbox can be described by an overdefined system of algebraic equations (true with probability 1). We show
Python Sampling Bias Maximum Entropy Discussion
"... distributions sharing true marginals True Distribution Subset with no higher order interactions ..."
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distributions sharing true marginals True Distribution Subset with no higher order interactions
Boosting in the limit: Maximizing the margin of learned ensembles
 In Proceedings of the Fifteenth National Conference on Artificial Intelligence
, 1998
"... The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has shown that the Adaboost algorithm is particularly effective at producing ensembles with large minimum margins, and theory s ..."
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Cited by 124 (0 self)
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Adaboost. However, these algorithms do not always yield better generalization performance. In fact, more often the opposite is true. We report on a series of controlled experiments which show that no simple version of the minimummargin story can be complete. We conclude that the crucial question as to why
The Marginal Utility of Income
 Journal of Public Economics
, 2008
"... In normative public economics it is crucial to know how fast the marginal utility of income declines as income increases. One needs this parameter for costbenefit analysis, for optimal taxation and for the (Atkinson) measurement of inequality. We estimate this parameter using four large crosssecti ..."
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Cited by 71 (6 self)
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In normative public economics it is crucial to know how fast the marginal utility of income declines as income increases. One needs this parameter for costbenefit analysis, for optimal taxation and for the (Atkinson) measurement of inequality. We estimate this parameter using four large cross
Margins and Errors
 Inquiry
, 2013
"... Recently, Timothy Williamson has argued that considerations about margins of errors can generate a new class of cases where agents have justified true beliefs without knowledge. I think this is a great argument, and it has a number of interesting philosophical conclusions. In this note I'm goi ..."
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Cited by 2 (0 self)
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Recently, Timothy Williamson has argued that considerations about margins of errors can generate a new class of cases where agents have justified true beliefs without knowledge. I think this is a great argument, and it has a number of interesting philosophical conclusions. In this note I
Marginal inference in MRFs using FrankWolfe
 In NIPS Workshop on Greedy Optimization, FrankWolfe and Friends
, 2013
"... We introduce an algorithm, based on the FrankWolfe technique (conditional gradient), for performing marginal inference in undirected graphical models by repeatedly performing MAP inference. It minimizes standard Bethestyle convex variational objectives for inference, leverages known MAP algorith ..."
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Cited by 4 (0 self)
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algorithms as black boxes, and offers a principled means to construct sparse approximate marginals for higharity graphs. We also offer intuition and empirical evidence for a relationship between the entropy of the true marginal distribution of the model and the convergence rate of the algorithm. We
Results 1  10
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1,050