The ACM SIGKDD conference has established itself as the premier international conference on knowledge discovery and data mining. The SIGKDD conference will feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, and panels, as well as the KDD Cup competition.The Industrial/Government Applications Track of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining will highlight challenges, lessons, concerns, and research issues arising out of deploying applications of KDD technology.

COLT invites submissions of papers addressing the theoretical modeling and analysis of all aspects of learning and empirical inference. COLT strongly support a broad definition of learning theory, including analysis of learning algorithms and their generalization ability, computational complexity of learning, Bayesian analysis, statistical mechanics of learning systems, optimization procedures for learning, inductive inference, Boolean function learning, inductive logic programming, learning in planning and control, on-line learning and relative loss bounds. COLT welcome theoretical papers about learning that do not fit into the above categories. COLT are particularly interested in papers that include viewpoints that are new to the COLT community.

The NIPS Conference features a single track program, with contributions from a large number of intellectual communities. Presentation topics include: Algorithms and Architectures; Applications; Brain Imaging; Cognitive Science and Artificial Intelligence; Control and Reinforcement Learning; Emerging Technologies; Learning Theory; Neuroscience; Speech and Signal Processing; and Visual Processing.

AAAI is the AAAI Conference on Artificial Intelligence (AI). The purpose of this conference is to promote research in AI and scientific exchange among AI researchers, practitioners, scientists and engineers in related disciplines. AAAI will have multiple technical tracks, student abstracts, invited speakers, exhibit programs, and an evening reception with demos and posters, all selected according to the highest reviewing standards. AAAI welcomes submissions on mainstream AI topics as well as novel cross-cutting work in related areas.

IJCAI is the International Joint Conference on Artificial Intelligence, the main international gathering of researchers in AI. Held biennially in odd-numbered years since 1969, IJCAI is sponsored jointly by IJCAI and the national AI societie(s) of the host nation(s).

The IEEE International Conference on Data Mining series (ICDM) has established itself as the world's premier research conference in data mining. It provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications. In addition, ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high performance computing.

In keeping with the original topic of Inductive Logic Programming, while reflecting also the broadening scope of the field, authors are invited to submit papers presenting original results in all aspects of learning logic programs, learning in first-order logic and in other logic-based knowledge representation frameworks. Papers on logical aspects of multi-relational learning and data mining are also welcome. Typical, but not exclusive, topics of interest for submissions include: Theoretical aspects (logical foundations, computational and/or statistical learning theory, specialization and generalization operators, etc.) of learning in logic (logic programs, constraint logic programs, Datalog, first-order logic, description logics, higher-order logic, etc.), or from relational or graph databases Algorithmic and implementation aspects of learning in logic including the design of algorithms along with theoretical and/or empirical analysis, probabilistic and statistical approaches, distance and kernel-based methods, relational reinforcement learning, learning from multi-relational databases, scalability issues, inductive databases, link discovery, multi-instance learning, etc., Applications including, but not restricted to multi-relational learning from structured (e.g., labeled graphs, tree patterns) and semi-structured data (e.g., XML documents), in areas of science (bioinformatics, cheminformatics, medical informatics, etc.),natural language processing (computational linguistics, relational text and web mining etc.), engineering or the arts. Papers emphasizing new topics related to learning in logic, as well as to logical foundations of multi-relational learning and data mining are especially encouraged.

UAI encourages submissions that report on theoretical or methodological advances in representation, automated reasoning, learning, decision making and knowledge acquisition under uncertainty. Submissions reporting on the novel and insightful application of these techniques within intelligent systems are also strongly encouraged. Examples of such applications include, but are not limited to, computational biology, computer vision, medical systems, multi-agent systems, self-managing computer systems, and sensor networks.

Data mining is an important tool in science, engineering, industrial processes, health care, business, and medicine. The datasets in these fields are large, complex, and often noisy. Extracting knowledge requires the use of sophisticated, high-performance and principled analysis techniques and algorithms, based on sound theoretical and statistical foundations. These techniques in turn require powerful visualization technologies; implementations that must be carefully tuned for performance; software systems that are usable by scientists, engineers, and physicians as well as researchers; and infrastructures that support them. This conference provides a venue for researchers who are addressing these problems to present their work in a peer-reviewed forum. It also provides an ideal setting for graduate students and others new to the field to learn about cutting-edge research by hearing outstanding invited speakers and attending tutorials (included with conference registration). A set of focused workshops are also held on the last day of the conference. The proceedings of the conference are published in archival form, and are also made available on the SIAM web site.