Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subty** …

Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality
of ML models becomes paramount for two reasons:(a) a model can be a business …

This paper surveys quantum learning theory: the theoretical aspects of machine learning
using quantum computers. We describe the main results known for three models of learning …

On the Complexity of Dualization of Monotone Disjunctive Normal Forms Page 1 Ž . JOURNAL
OF ALGORITHMS 21, 618628 1996 ARTICLE NO. 0062 On the Complexity of Dualization of …

We establish hardness versus randomness trade-offs for a broad class of randomized
procedures. In particular. we create efficient nondeterministic simulations of bounded round …

The field of grammatical inference (also known as grammar induction) is transversal to a
number of research areas including machine learning, formal language theory, syntactic and …

This book is an accessible introduction to complexity theory and cryptology, two closely
related areas in theoretical computer science. Based on courses taught at Heinrich-Heine …

Abstract In 1950, John Nash sent a remarkable letter to the National Security Agency, in
which—seeking to build theoretical foundations for cryptography—he all but formulated what …

Several data mining problems can be formulated as problems of finding maximally specific
sentences that are interesting in a database. We first show that this problem has a close …