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 …

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 …

Data mining can be viewed, in many instances, as the task of computing a representation of
a theory of a model or a database, in particular by finding a set of maximally specific …