Cryptographic hardness of distribution-specific learning
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
How fast can a threshold gate learn?
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Unreliable failure detectors for reliable distributed systems
Journal of the ACM (JACM)
A new algorithm for minimizing convex functions over convex sets
Mathematical Programming: Series A and B
Machine Learning
Machine Learning
Solving convex programs by random walks
Journal of the ACM (JACM)
Universal semantic communication I
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Every problem has a weakest failure detector
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Universal semantic communication
Universal semantic communication
A theory of goal-oriented communication
Journal of the ACM (JACM)
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Previous works [11, 6] introduced a model of semantic communication between a "user" and a "server," in which the user attempts to achieve a given goal for communication. They show that whenever the user can sense progress, there exist universal user strategies that can achieve the goal whenever it is possible for any other user to reliably do so. A drawback of the actual constructions is that the users are inefficient: they enumerate protocols until they discover one that is successful, leading to the potential for exponential overhead in the length of the desired protocol. Goldreich et al. [6] conjectured that this overhead could be reduced to a polynomial dependence if we restricted our attention to classes of sufficiently simple user strategies and goals. In this work, we are able to obtain such universal strategies for some reasonably general special cases by establishing an equivalence between these special cases and the usual model of mistake-bounded on-line learning [3, 15]. This equivalence also allows us to see the limits of constructing universal users based on sensing and motivates the study of sensing with richer kinds of feedback. Along the way, we also establish a new lower bound for the "beliefs model" [12], which demonstrates that constructions of efficient users in that framework rely on the existence of a common "belief" under which all of the servers in a class are designed to be efficient.