TY - GEN
T1 - Robust inference and local algorithms
AU - Mansour, Yishay
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2015.
PY - 2015
Y1 - 2015
N2 - We introduce a new feature to inference and learning which we call robustness. By robustness we intuitively model the case that the observation of the learner might be corrupted. We survey a new and novel approach to model such possible corruption as a zero-sum game between an adversary that selects the corruption and a leaner that predict the correct label. The corruption of the observations is done in a worse-case setting, by an adversary, where the main restriction is that the adversary is limited to use one of a fixed know class of modification functions. The main focus in this line of research is on efficient algorithms both for the inference setting and for the learning setting. In order to be efficient in the dimension of the domain, one cannot hope to inspect all the possible inputs. For this, we have to invoke local computation algorithms, that inspect only a logarithmic fraction of the domain per query.
AB - We introduce a new feature to inference and learning which we call robustness. By robustness we intuitively model the case that the observation of the learner might be corrupted. We survey a new and novel approach to model such possible corruption as a zero-sum game between an adversary that selects the corruption and a leaner that predict the correct label. The corruption of the observations is done in a worse-case setting, by an adversary, where the main restriction is that the adversary is limited to use one of a fixed know class of modification functions. The main focus in this line of research is on efficient algorithms both for the inference setting and for the learning setting. In order to be efficient in the dimension of the domain, one cannot hope to inspect all the possible inputs. For this, we have to invoke local computation algorithms, that inspect only a logarithmic fraction of the domain per query.
UR - https://www.scopus.com/pages/publications/84943616006
U2 - 10.1007/978-3-662-48057-1_4
DO - 10.1007/978-3-662-48057-1_4
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AN - SCOPUS:84943616006
SN - 9783662480564
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 60
BT - Mathematical Foundations of Computer Science 2015 - 40th International Symposium, MFCS 2015, Proceedings
A2 - Pighizzini, Giovanni
A2 - Italiano, Giuseppe F.
A2 - Sannella, Donald T.
PB - Springer Verlag
T2 - 40th International Symposium on Mathematical Foundations of Computer Science, MFCS 2015
Y2 - 24 August 2015 through 28 August 2015
ER -