International Workshop All '86 on Analogical and inductive inference
Learning regular sets from queries and counterexamples
Information and Computation
Monotonic and non-monotonic inductive inference
New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Language learning in dependence on the space of hypotheses
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Journal of Computer and System Sciences
Automata on Infinite Objects and Church's Problem
Automata on Infinite Objects and Church's Problem
Avoiding coding tricks by hyperrobust learning
Theoretical Computer Science
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Automatic Presentations of Structures
LCC '94 Selected Papers from the International Workshop on Logical and Computational Complexity
Automatic structures: overview and future directions
Journal of Automata, Languages and Combinatorics - Special issue: Selected papers of the workshop weighted automata: Theory and applications (Dresden University of Technology (Germany), March 4-8, 2002)
Formal language identification: query learning vs. gold-style learning
Information Processing Letters
Learning indexed families of recursive languages from positive data: A survey
Theoretical Computer Science
Robust separations in inductive inference
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Learnability of automatic classes
Journal of Computer and System Sciences
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One of the most important paradigms in the inductive inference literature is that of robust learning. This paper adapts and investigates the paradigm of robust learning to learning languages from positive data. Broadening the scope of that paradigm is important: robustness captures a form of invariance of learnability under admissible transformations on the object of study; hence, it is a very desirable property. The key to defining robust learning of languages is to impose that the latter be automatic, that is, recognisable by a finite automaton. The invariance property used to capture robustness can then naturally be defined in terms of first-order definable operators, called translators. For several learning criteria amongst a selection of learning criteria investigated either in the literature on explanatory learning from positive data or in the literature on query learning, we characterise the classes of languages all of whose translations are learnable under that criterion.