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Purifying data by machine learning with certainty levels
2010
Proceedings of the Third International Workshop on Reliability, Availability, and Security - WRAS '10
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This paper introduces the use of a certainty level measure to obtain better classification capability in the presence of corrupted data items. ...
This method is of independent interest, that of significantly improving the classification of the random forest machine learning technique in less severe settings. ...
In the Probably Approximately Correct (PAC) learning framework, Valiant (Valiant, 1984) introduced the notion of PAC learning in the presence of malicious noise. ...
doi:10.1145/1953563.1953567
dblp:conf/podc/DolevLY10
fatcat:5tsho3pw6vcdddhkvxp2ykupjm
Page 5392 of Mathematical Reviews Vol. , Issue 94i
[page]
1994
Mathematical Reviews
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
BELL; Murray Hill, NJ);
Li, Ming [Li, Ming"] (3-WTRL-C; Waterloo, ON)
Learning in the presence of malicious errors. ...
The “PAC” model of learning was proposed by L. G. Valiant [Comm. ...
Learning Logic programs with random classification noise
[chapter]
1997
Lecture Notes in Computer Science
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We review the polynomial PAC learnability of nonrecursive, determinate, constant-depth Horn clauses in the presence of such noise. ...
Also, we show that arbitrary nonrecursive Horn clauses with forest background knowledge remain polynomially PAC learnable in the presence of noise. ...
Learning Conjunctions from Noisy Data Angluin and Laird 1] gave the rst algorithm for PAC learning conjunctions from data with random classi cation noise. ...
doi:10.1007/3-540-63494-0_63
fatcat:ecttkvrzo5eytjdjnpidqai5lm
Federated Learning from Small Datasets
[article]
2021
arXiv
pre-print
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This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. ...
Often, however, local datasets are so small that local objectives differ greatly from the global objective, resulting in federated learning to fail. ...
Similarly, the malicious client can infer upon the presence of data points with certain attributes in the dataset (Ateniese et al., 2015) . ...
arXiv:2110.03469v2
fatcat:7ucgfn2qqbeb3dartwl3aic3bi
On the Sample Complexity of Adversarial Multi-Source PAC Learning
[article]
2020
arXiv
pre-print
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We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. ...
It is known that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training ...
Acknowledgements Dan Alistarh is supported in part by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML). ...
arXiv:2002.10384v2
fatcat:sd4daitwevhnxmcgyra3kravhu
On learning visual concepts and DNF formulae
1996
Machine Learning
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We consider the problem of learning DNF formulae in the mistake-bound and the PAC models. ...
Unlike previous learnability results for DNF (and CNF) formulae, these subclasses are not limited in the number of terms or in the number of variables per term; yet, they contain the subclasses of k-DNF ...
We thank the referees for helpful suggestions that improved the presentation of this work. Eyal Kushilevitz was supported by research contracts ONR-N0001491-J-1981 and NSF-CCR-90-07677. ...
doi:10.1007/bf00117833
fatcat:vdnxtlfmwbbynk77npn4lmocm4
On learning visual concepts and DNF formulae
1993
Proceedings of the sixth annual conference on Computational learning theory - COLT '93
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We consider the problem of learning DNF formulae in the mistake-bound and the PAC models. ...
Unlike previous learnability results for DNF (and CNF) formulae, these subclasses are not limited in the number of terms or in the number of variables per term; yet, they contain the subclasses of k-DNF ...
We thank the referees for helpful suggestions that improved the presentation of this work. Eyal Kushilevitz was supported by research contracts ONR-N0001491-J-1981 and NSF-CCR-90-07677. ...
doi:10.1145/168304.168362
dblp:conf/colt/KushilevitzR93
fatcat:4ivza5tdqrgyvcm7obfq4clayi
Open problems in the security of learning
2008
Proceedings of the 1st ACM workshop on Workshop on AISec - AISec '08
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In this paper, we present three broad research directions towards the end of developing truly secure learning. ...
Machine learning has become a valuable tool for detecting and preventing malicious activity. ...
This work was supported in part by the Team for Research in Ubiquitous Secure Technology (TRUST), which receives support from the National Science Foundation ( ...
doi:10.1145/1456377.1456382
dblp:conf/ccs/BarrenoBCJNRST08
fatcat:4uk7kufh4zevfgxkvhz7t4qvm4
Table of contents
2019
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
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Moura, Carnegie Mellon University, United States MA1.5: PAC LEARNING FROM DISTRIBUTED DATA IN THE PRESENCE OF ...............................186 MALICIOUS NODES Zhixiong Yang, Waheed Bajwa, Rutgers University-New ...
Brunswick, United States TM1: Learning over Graphs TM1.1: LEARNING SPARSE HYPERGRAPHS FROM DYADIC RELATIONAL DATA ......................216 Mario Coutino, TU Delft, Netherlands; Sundeep Chepuri, Indian ...
Joaquin Míguez, Universidad Carlos III de Madrid, Spain; Lucas Lacasa, Queen Mary University of London, United Kingdom; Universidad Carlos III de Madrid, Spain; Inés P. ...
doi:10.1109/camsap45676.2019.9022485
fatcat:eiz3bmwzdrfojc36xlrzbrppdq
Fog computing security: a review of current applications and security solutions
2017
Journal of Cloud Computing: Advances, Systems and Applications
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In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. ...
For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. ...
However, the presence of such private data puts the Fog platform in a sensitive position. ...
doi:10.1186/s13677-017-0090-3
fatcat:g3q3q3ctf5coteaycnceib522u
Knowing what doesn't matter: exploiting the omission of irrelevant data
1997
Artificial Intelligence
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In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. ...
PAC model-viz., decision trees and DNF formulae-are trivial to learn in this setting. ...
PAC-learning decision trees in the presence of classification noise follows from the techniques of the previous theorem. Namely, first let k > 8/( 1 -2a)* ln(2s/6) (which improves on Eq. ...
doi:10.1016/s0004-3702(97)00048-9
fatcat:5ldpntwiyje6rhymbd6h476li4
Cybersecurity in PACS and Medical Imaging: an Overview
2020
Journal of digital imaging
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From a practical perspective, PACS specific security measures must be implemented together with the measures applicable to the IT infrastructure as a whole, in order to prevent incidents such as PACS systems ...
The article concludes with a discussion of gaps in the body of published literature and a summary. ...
Eichelberg reports a grant from VISUS Health IT, during the conduct of the study. Mr. Kleber and Dr. ...
doi:10.1007/s10278-020-00393-3
pmid:33123867
fatcat:6cf63xnkr5feznvca5e6a3joqe
A neuroidal architecture for cognitive computation
[chapter]
1998
Lecture Notes in Computer Science
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Attribute-efficient learning algorithms, which allow learning from few examples in large dimensional systems, are fundamental to the approach. ...
Its aim is to exploit to the full those aspects of computational learning that are known to offer powerful solutions in the acquisition and maintenance of robust knowledge bases. ...
I am grateful to Roni Khardon, Dan Roth, and Rocco Servedio and several referees for their helpful comments on various versions of this paper. ...
doi:10.1007/bfb0055091
fatcat:b6534lxzkjcdrffkcvy25wiy6q
A neuroidal architecture for cognitive computation
2000
Journal of the ACM
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Attribute-efficient learning algorithms, which allow learning from few examples in large dimensional systems, are fundamental to the approach. ...
Its aim is to exploit to the full those aspects of computational learning that are known to offer powerful solutions in the acquisition and maintenance of robust knowledge bases. ...
I am grateful to Roni Khardon, Dan Roth, and Rocco Servedio and several referees for their helpful comments on various versions of this paper. ...
doi:10.1145/355483.355486
fatcat:ydvy6dok6ra2vcbnuqngi6fiey
Mechanism Design for Cost Optimal PAC Learning in the Presence of Strategic Noisy Annotators
[article]
2012
arXiv
pre-print
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We consider the problem of Probably Approximate Correct (PAC) learning of a binary classifier from noisy labeled examples acquired from multiple annotators (each characterized by a respective classification ...
For this scenario, we design a cost optimal procurement auction mechanism along the lines of Myerson's optimal auction design framework in a non-trivial manner. ...
this algorithm in the presence of n noisy annotators. ...
arXiv:1210.4859v1
fatcat:ajlacnzry5f4lnfdudf4ndcjf4
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