The Design Of Competitive Online Algorithms Via A Primal Dual Approach
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The Design of Competitive Online Algorithms Via a Primal-Dual Approach
Extends the primal-dual method to the setting of online algorithms, and shows its applicability to a wide variety of fundamental problems.
Algorithmic Learning Theory
Author: Marcus Hutter
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-27
This volume contains the papers presented at the 21st International Conf- ence on Algorithmic Learning Theory (ALT 2010), which was held in Canberra, Australia, October 6–8, 2010. The conference was co-located with the 13th - ternational Conference on Discovery Science (DS 2010) and with the Machine Learning Summer School, which was held just before ALT 2010. The tech- cal program of ALT 2010, contained 26 papers selected from 44 submissions and ?ve invited talks. The invited talks were presented in joint sessions of both conferences. ALT 2010 was dedicated to the theoretical foundations of machine learning and took place on the campus of the Australian National University, Canberra, Australia. ALT provides a forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as inductive inference, universal prediction, teaching models, grammatical inference, formal languages, inductive logic programming, query learning, complexity of learning, on-line learning and relative loss bounds, semi-supervised and unsupervised learning, clustering,activelearning,statisticallearning,supportvectormachines,Vapnik- Chervonenkisdimension,probablyapproximatelycorrectlearning,Bayesianand causal networks, boosting and bagging, information-based methods, minimum descriptionlength,Kolmogorovcomplexity,kernels,graphlearning,decisiontree methods, Markov decision processes, reinforcement learning, and real-world - plications of algorithmic learning theory. DS 2010 was the 13th International Conference on Discovery Science and focused on the development and analysis of methods for intelligent data an- ysis, knowledge discovery and machine learning, as well as their application to scienti?c knowledge discovery. As is the tradition, it was co-located and held in parallel with Algorithmic Learning Theory.
Theory and Applications of Models of Computation
This book constitutes the refereed proceedings of the 16th Annual Conference on Theory and Applications of Models of Computation, TAMC 2020, held in Changsha, China, in October 2020. The 37 full papers were carefully reviewed and selected from 83 submissions. The main themes of the selected papers are computability, complexity, algorithms, information theory and their extensions to machine learning theory and foundations of artificial intelligence.