They have been of interest to philosophy and scientific endeavour since the ancient times. To model this process, a number of statistical models have been developed. Despite the superficial differences, there exist common ideas which make it worthwhile to record a snapshot of where we are, where we want to go, and how we plan to achieve this. At the same time, technological applications of induction in machine learning systems have extensively been explored algorithmically, highlighting the importance of issues which had typically not been the concern of philosophy. Practical problems of inference are concerned with noise in the data and with the issue of overfitting, i.e. extracting more structure from the data than is supported by it.
Algorithms have to select a model from a large set of potential interpretations of the data.
Model averaging, noise robustness, overfitting, capacity and other concepts play a central role in many of the theories. The workshop focuses on the long-term perspective of Machine Learning and its impact on Computer Science, Statistics, Mathematics and Philosophy, rather than on the latest implementations or sophisticated technical details. Participants are encouraged to stimulate the discussion with a single slide that contains what they consider the crucial open problem, insight, or idea. Each half day will be devoted to one topic, starting with a tutorial. Attendees will then have the possibility to contribute in discussions, with short impromptu talks, or by presenting open problems.