Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.

• active learning
• inductive inference
• online learning algorithms
• reinforcement learning
• sequential decision making
• adversary models
• boolean function learning
• clustering
• evolutionary algorithms
• interactive learning
• local search
• models of learning
• online learning theory
• optimization
• perceptron
• query learning
• sample complexity and generalization bounds
• structured prediction
• semi-supervised learning
• structured prediction
• unsupervised learning

• Montanuniversität Leoben , Leoben, Austria

  Ronald Ortner

• Ruhr-Uni-Bochum , Bochum, Germany

  Hans Ulrich Simon

• University of Regina , Regina, Canada

  Sandra Zilles

Policies and ethics