Alan Fern

1148, Kelly Engineering Center
Corvallis, OR 97333
(541) 737-9202


 ICML 2007 WORKSHOPS: June 24, 2007

  Chair: Alan Fern  


There will be 4 workshops held at the end of ICML-2007 on Sunday, June 24, 2007 at Oregon State University, Corvallis, Oregon, U.S.A.

Please refer to the workshop web-pages below regarding submission deadlines and formats.

Workshop participants will not be given workshop proceedings in a hardcopy format. Instead, organizers of the workshops will make the proceedings available on their website prior to the conference.

Accepted Workshops

  • Challenges and Applications of Grammar Induction (CAGI'07)-- Grammar Induction (GI), also known as Grammatical Inference, is about learning grammars from data. A well-known important application of GI is natural language learning, but it is applicable in a much broader sense to the problem of learning structural models from data. The CAGI workshop aims at highlighting current challenges in GI with a special focus on applicability issues including. See web-site for more details.

  • Induction of Process Models-- This workshop focuses on learning process models from observations and background knowledge. Although a large literature on time-series analysis exists, it emphasizes descriptive models that ignore the underlying mechanisms of the studied system. In contrast, the workshop centers on knowledge-aware learning approaches that refer to relevant scientific concepts. These methods work to uncover the mechanisms that generate observed behavior, to identify plausible state-transition networks, or to induce pathways that depict flux within the system.  The resulting models may characterize behavior in qualitative, quantitative, or mixed representations with an emphasis on comprehensibility.  Key research directions in process-based modeling include knowledge-rich induction, learning in expressive languages, and modeling temporal and spatial data. See web-site for more details.

  • Constrained Optimization and Learning with Structured Outputs-- In recent years, there has been a great deal of work relating constrained optimization problems with machine learning in structured output spaces. This has given rise to a number of novel and powerful approaches to solving some extremely difficult machine learning problems. The objective of this workshop is to bring together researchers in both of these areas, thereby encouraging furthercollaboration and increasing awareness of the issues at hand in both communities. See web-site for more details.