This volumecontains paperspresentedatthe 20thAnnualConferenceonLea- ing Theory (previously known as the Conference on Computational Learning Theory) held in San Diego, USA, June 13-15, 2007, as part of the 2007 Fed- ated Computing Research Conference (FCRC). The Technical Program contained 41 papers selected from 92 submissions, 5 open problems selected from among 7 contributed, and 2 invited lectures. The invited lectures were givenby Dana Ron on PropertyTesting: A Learning T- oryPerspective, andbySantoshVempalaon SpectralAlgorithmsforLearning and Clustering. The abstracts of these lectures are included in this volume. The Mark Fulk Award is presented annually for the best paper co-authored by a student. The student selected this year was Samuel E. Moelius III for the paper U-Shaped, Iterative, and Iterative-with-Counter Learning co-authored with John Case. This year, student awards were also granted by the Machine LearningJournal.Wehavethereforebeenabletoselecttwomorestudentpapers forprizes.Thestudents selectedwereLev Reyzinforthe paper LearningLarge- Alphabet and Analog Circuits with Value Injection Queries (co-authored with Dana Angluin, James Aspnes, and Jiang Chen), and Jennifer Wortman for the paper Regret to the Best vs. Regret to the Average (co-authored with Eyal Even-Dar, Michael Kearns, and Yishay Mansour). The selected papers cover a wide range of topics, including unsupervised, semisupervisedand activelearning, statistical learningtheory, regularizedlea- ing, kernel methods and SVM, inductive inference, learning algorithms and l- itations on learning, on-line and reinforcement learning. The last topic is part- ularly well represented, covering alone more than one-fourth of the total."

In der aktuellen Debatte uber die Rolle von Unternehmen in der sich wandelnden Gesellschaft stellt sich immer starker die Frage nach der sozialen Verantwortung. Experten aus Wissenschaft und Praxis untersuchen auf der Grundlage einer reprasentativen Befragung von mehr als 2.000 in der Schweiz tatigen Unternehmen das gemeinnutzige Engagement dieser Unternehmen und ihrer Mitarbeiter. Ausserdem werden anhand von Fallstudien und Evaluationen konkreter Corporate-Volunteering(CV)-Programme wichtige Erkenntnisse zur Einbettung und Umsetzung von CV-Aktivitaten in Betrieben dargestellt. Untersucht werden dabei die Beweggrunde der unterschiedlichen Teilnehmergruppen, die strukturelle und kulturelle Einbettung im Unternehmen sowie die Rahmenbedingungen fur erfolgversprechende Kooperationen mit NPOs. Abschliessend werden Handlungsfelder aufgezeigt und Anregungen zur Umsetzung von CV in der Unternehmenspraxis gegeben.

This book highlights new and original contributions on Graph Theory and Combinatorial Optimization both from the theoretical point of view and from applications in all fields. The book chapters describe models and methods based on graphs, structural properties, discrete optimization, network optimization, mixed-integer programming, heuristics, meta-heuristics, math-heuristics, and exact methods as well as applications. The book collects selected contributions from the CTW2020 international conference (18th Cologne-Twente Workshop on Graphs and Combinatorial Optimization), held online on September 14-16, 2020. The conference was organized by IASI-CNR with the contribution of University of Roma Tre, University Roma Tor Vergata, and CNRS-LIX and with the support of AIRO. It is addressed to researchers, PhD students, and practitioners in the fields of Graph Theory, Discrete Mathematics, Combinatorial Optimization, and Operations Research.

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.