Robust inference and local algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationMathematical Foundations of Computer Science 2015 - 40th International Symposium, MFCS 2015, Proceedings
EditorsGiovanni Pighizzini, Giuseppe F. Italiano, Donald T. Sannella
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783662480564
StatePublished - 2015
Event40th International Symposium on Mathematical Foundations of Computer Science, MFCS 2015 - Milan, Italy
Duration: 24 Aug 201528 Aug 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference40th International Symposium on Mathematical Foundations of Computer Science, MFCS 2015


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