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ECAI 2020
This book presents the proceedings of the 24th European Conference on Artificial Intelligence (ECAI 2020), held in Santiago de Compostela, Spain, from 29 August to 8 September 2020. The conference was postponed from June, and much of it conducted online due to the COVID-19 restrictions. The conference is one of the principal occasions for researchers and practitioners of AI to meet and discuss the latest trends and challenges in all fields of AI and to demonstrate innovative applications and uses of advanced AI technology. The book also includes the proceedings of the 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) held at the same time. A record number of ...
Advances in Knowledge Discovery and Data Mining
This book constitutes the refereed proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2001, held in Hong Kong, China in April 2001. The 38 revised full papers and 22 short papers presented were carefully reviewed and selected from a total of 152 submissions. The book offers topical sections on Web mining, text mining, applications and tools, concept hierarchies, feature selection, interestingness, sequence mining, spatial and temporal mining, association mining, classification and rule induction, clustering, and advanced topics and new methods.
Veridical Data Science
Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to...
Machine Learning from Weak Supervision
Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly su...
AI 2007: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 20th Australian Joint Conference on Artificial Intelligence, AI 2007, held in Gold Coast, Australia, in December 2007.The 58 revised full papers and 40 revised short papers presented together with the extended abstracts of three invited speeches were carefully reviewed and selected from 194 submissions. The papers are organized in topical sections on a broad range of subjects.
PRICAI 2004: Trends in Artificial Intelligence
The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is a biennial international event which focuses on Artificial Intelligence (AI) theories and technologies, and their applications which are of social and economic importance for countries in the Pacific Rim region. Seven earlier conferences were held in: Nagoya, Japan (1990); Seoul, Korea (1992); Beijing, China (1994); Cairns, Australia (1996); Singapore (1998); Melbourne, Australia (2000); and Tokyo, Japan (2002). PRICAI 2004 was the eigth in the series and was held in Auckland, New Zealand in August 2004. PRICAI 2004 had attracted a historical record number of submissions, a total of 356 papers. After careful revi...
AI 2008: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 21th Australasian Joint Conference on Artificial Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008. The 42 revised full papers and 21 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on knowledge representation, constraints, planning, grammar and language processing, statistical learning, machine learning, data mining, knowledge discovery, soft computing, vision and image processing, and AI applications.
PRICAI 2014: Trends in Artificial Intelligence
This book constitutes the refereed proceedings of the 13th Pacific Rim Conference on Artificial Intelligence, PRICAI 2014, held in Gold Coast, Queensland, Australia, in December 2014. The 74 full papers and 20 short papers presented in this volume were carefully reviewed and selected from 203 submissions. The topics include inference; reasoning; robotics; social intelligence. AI foundations; applications of AI; agents; Bayesian networks; neural networks; Markov networks; bioinformatics; cognitive systems; constraint satisfaction; data mining and knowledge discovery; decision theory; evolutionary computation; games and interactive entertainment; heuristics; knowledge acquisition and ontology; knowledge representation, machine learning; multimodal interaction; natural language processing; planning and scheduling; probabilistic.
Learning Theory from First Principles
A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates...
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key...
