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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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Proceedings Article
Efficient spectral feature selection with minimum redundancy
Zheng Zhao,Lei Wang,Huan Liu +2 more
TL;DR: This work proposes a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model derived from a formulation based on a sparse multi-output regression with a L2,1-norm constraint.
Posted Content
Preserving Statistical Validity in Adaptive Data Analysis
TL;DR: It is shown that, surprisingly, there is a way to estimate an exponential in n number of expectations accurately even if the functions are chosen adaptively, and this gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates.
Journal ArticleDOI
Differentially Private Data Publishing and Analysis: A Survey
TL;DR: This survey compares the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy.
MonographDOI
Foundations of Data Science
TL;DR: Computer science as an academic discipline began in the 1960’s with emphasis on programming languages, compilers, operating systems, and the mathematical theory that supported these areas, but today, a fundamental change is taking place and the focus is more on applications.
Journal ArticleDOI
Language evolution by iterated learning with bayesian agents.
TL;DR: The role of iteratedLearning is clarified in explanations of linguistic universals and a formal connection between constraints on language acquisition and the languages that come to be spoken is provided, suggesting that information transmitted via iterated learning will ultimately come to mirror the minds of the learners.