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Learning from Multiple Sources

Learning from Multiple Sources with Applications to Robotics

Learning from multiple sources denotes the problem of jointly learning from a set of (partially) related learning problems / views / tasks. This general concept underlies several subfields receiving increasing interest from the machine learning community, which differ in terms of the assumptions made about the dependency structure between learning problems. In particular, the concept includes topics such as data fusion, transfer learning, multitask learning, multiview learning, and learning under covariate shift. Several approaches for inferring and exploiting complex relationships between data sources have been presented, including both generative and discriminative approaches.

The workshop will provide a unified forum for cutting edge research on learning from multiple sources; the workshop will examine the general concept, theory and methods, and will also examine robotics as a natural application domain for learning from multiple sources. The workshop will address methodological challenges in the different subtopics and further interaction between them. The intended audience is researchers working in fields of multi-modal learning, data fusion, and robotics.

The Workshop homepage can be found at

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Brenna D. Argall, Bertrand Douillard, et al. Brenna D. Argall, Bertrand Douillard, Amrish S. Kapoor, Jesús Martínez-Gómez, Arman Melkumyan

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