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Agnostically learning under permutation invariant distributions
, 2013
"... We generalize algorithms from computational learning theory that are successful under the uniform distribution on the Boolean hypercube {0, 1} n to algorithms successful on permutation invariant distributions, distributions where the probability mass remains constant upon permutations in the instanc ..."
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Cited by 3 (0 self)
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in the instances. While the tools in our generalization mimic those used for the Boolean hypercube, the fact that permutation invariant distributions are not product distributions presents a significant obstacle. Under the uniform distribution, halfspaces can be agnostically learned in polynomial time for constant
Agnostic learning of disjunctions on symmetric distributions
 arXiv, CoRR
"... We consider the problem of approximating and learning disjunctions (or equivalently, conjunctions) on symmetric distributions over {0, 1}n. Symmetric distributions are distributions whose PDF is invariant under any permutation of the variables. We give a simple proof that for every symmetric distrib ..."
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Cited by 5 (3 self)
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We consider the problem of approximating and learning disjunctions (or equivalently, conjunctions) on symmetric distributions over {0, 1}n. Symmetric distributions are distributions whose PDF is invariant under any permutation of the variables. We give a simple proof that for every symmetric
Understanding FaultTolerant Distributed Systems
 COMMUNICATIONS OF THE ACM
, 1993
"... We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design ..."
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Cited by 374 (23 self)
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We propose a small number of basic concepts that can be used to explain the architecture of faulttolerant distributed systems and we discuss a list of architectural issues that we find useful to consider when designing or examining such systems. For each issue we present known solutions and design
Agnostically learning halfspaces
, 2005
"... We give the first algorithm that (under distributional assumptions) efficiently learns halfspaces in the notoriously difficult agnostic framework of Kearns, Schapire, & Sellie, where a learner is given access to labeled examples drawn from a distribution, without restriction on the labels (e.g ..."
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Cited by 97 (27 self)
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We give the first algorithm that (under distributional assumptions) efficiently learns halfspaces in the notoriously difficult agnostic framework of Kearns, Schapire, & Sellie, where a learner is given access to labeled examples drawn from a distribution, without restriction on the labels (e
A PTAS for Agnostically Learning Halfspaces
, 2015
"... We present a PTAS for agnostically learning halfspaces w.r.t. the uniform distribution on the d dimensional sphere. Namely, we show that for every µ> 0 there is an algorithm that runs in time poly d, 1, and is guaranteed to return a classifier with error at most (1 + µ)opt + , where opt is the er ..."
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We present a PTAS for agnostically learning halfspaces w.r.t. the uniform distribution on the d dimensional sphere. Namely, we show that for every µ> 0 there is an algorithm that runs in time poly d, 1, and is guaranteed to return a classifier with error at most (1 + µ)opt + , where opt
Agnostically Learning
"... We give the first algorithm that (under distributional assumptions) efficiently learns a halfspace in the notoriously difficult agnostic framework of Kearns, Schapire, & Sellie, where a learner is given access to labeled examples drawn from a distribution, without restriction on the labels (eq., ..."
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We give the first algorithm that (under distributional assumptions) efficiently learns a halfspace in the notoriously difficult agnostic framework of Kearns, Schapire, & Sellie, where a learner is given access to labeled examples drawn from a distribution, without restriction on the labels (eq
Agnostic bayesian learning of ensembles
 In Proceedings of The 31st International Conference on Machine Learning
, 2014
"... We propose a method for producing ensembles of predictors based on holdout estimations of their generalization performances. This approach uses a prior directly on the performance of predictors taken from a finite set of candidates and attempts to infer which one is best. Using Bayesian inference, ..."
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Cited by 1 (1 self)
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formance and does not assume that the data was actually generated from any of the predictors in the ensemble. Since the problem of finding the best (as opposed to the true) predictor among a class is known as agnostic PAClearning, we refer to our method as agnostic Bayesian learning. We also propose a method
Agnostic boosting
 In Proceedings of the 14th Annual Conference on Computational Learning Theory
, 2001
"... Martingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of misclassification noise. 1 ..."
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Cited by 34 (7 self)
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Martingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of misclassification noise. 1
Agnostic Learning of Geometric Patterns
 Journal of Computer and System Sciences
, 1997
"... Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a onedimensional visual image can be mapped to that of learning a onedimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient online agnostic learning ..."
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Cited by 29 (15 self)
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Goldberg, Goldman, and Scott demonstrated how the problem of recognizing a landmark from a onedimensional visual image can be mapped to that of learning a onedimensional geometric pattern and gave a PAC algorithm to learn that class. In this paper, we present an efficient online agnostic
Results 1  10
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