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 - http://www.scopus.com/inward/record.url?scp=84943616006&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-48057-1_4

DO - 10.1007/978-3-662-48057-1_4

M3 - פרסום בספר כנס

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

Y2 - 24 August 2015 through 28 August 2015

ER -