AbstractWe study learning in a modified EXACT model, where the oracles are corrupt and only few of the presented attributes are relevant. Both modifications were already studied in the literature [Dana Angluin, Mārtiņš Krikis, Learning with malicious membership queries and exceptions (extended abstract), in: COLT ’94: Proceedings of the Seventh Annual Conference on Computational Learning Theory, ACM Press, 1994, pp. 56–57 [3]; Dana Angluin, Mārtiņš Krikis, Robert H. Sloan, György Turán, Malicious omissions and errors in answers to membership queries, Machine Learning 28 (1997) 211–255; Laurence Bisht, Nader H. Bshouty, Lawrance Khoury, Learning with errors in answers to membership queries (extracted abstract), in: FOCS ’04: Proceedings of t...
In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learnin...
We consider a model where given an uncorrupted input an adversary can corrupt it to one out of m cor...
International audienceRecently, it has been shown that Machine Learning models can leak sensitive in...
AbstractWe consider the problem ofattribute-efficientlearning in query and mistake-bound models. Att...
AbstractWe consider the problem ofattribute-efficientlearning in query and mistake-bound models. Att...
AbstractWe study the power of two models of faulty teachers in Valiant’s PAC learning model and Angl...
AbstractThis paper addresses the problem of learning boolean functions in query and mistake-bound mo...
AbstractWe study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malici...
We consider exact learning of concepts using two types of query: extended equivalence queries, and m...
AbstractWe study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malici...
AbstractThe present work investigates Gold-style algorithmic learning from input–output examples whe...
AbstractA challenging problem within machine learning is how to make good inferences from data sets ...
Active learning algorithms automatically identify the salient and exemplar samples from large amount...
Active learning algorithms automatically identify the salient and exemplar samples from large amount...
We study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malicious omis...
In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learnin...
We consider a model where given an uncorrupted input an adversary can corrupt it to one out of m cor...
International audienceRecently, it has been shown that Machine Learning models can leak sensitive in...
AbstractWe consider the problem ofattribute-efficientlearning in query and mistake-bound models. Att...
AbstractWe consider the problem ofattribute-efficientlearning in query and mistake-bound models. Att...
AbstractWe study the power of two models of faulty teachers in Valiant’s PAC learning model and Angl...
AbstractThis paper addresses the problem of learning boolean functions in query and mistake-bound mo...
AbstractWe study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malici...
We consider exact learning of concepts using two types of query: extended equivalence queries, and m...
AbstractWe study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malici...
AbstractThe present work investigates Gold-style algorithmic learning from input–output examples whe...
AbstractA challenging problem within machine learning is how to make good inferences from data sets ...
Active learning algorithms automatically identify the salient and exemplar samples from large amount...
Active learning algorithms automatically identify the salient and exemplar samples from large amount...
We study the learning models defined in [D. Angluin, M. Krikis, R.H. Sloan, G. Turán, Malicious omis...
In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learnin...
We consider a model where given an uncorrupted input an adversary can corrupt it to one out of m cor...
International audienceRecently, it has been shown that Machine Learning models can leak sensitive in...