MLTP 2013 - International Workshop on Theory and Practice in Machine Learning

Several different forms of theoretical foundations for machine learning have been developed over the years for different settings. Examples include PAC and PAC-Bayesian learning, online learning, statistical learning theory, Bayesian learning theory, model selection using MML/MDL and Algorithmic Information Theory. These frameworks provide performance guarantees for certain categories of algorithms when applied to classes of problems. Although strong theoretical guarantees do not necessarily imply good empirical results, theory can arguably still provide principles and insight when developing new algorithms or applying them to new problems.
Conversely, there has been a recent and dramatic uptake in the application of machine learning techniques to an increasingly diverse range of problems. Arguably, some of the most successful algorithms on practical problems are not completely understood theoretically (e.g., random forests) or tend to be very ad hoc (e.g., the collections of techniques that won the Netflix prize). Extracting general principles and theories from these successes could help improve how quickly and easily new, practical problems are solved.
In this workshop we intend to discuss what makes a theory relevant for practical development of algorithms and how the gap between theory and practice can be decreased. We invite submission of 500-1000 words to peter.sunehag-AT-anu.edu.au on the following (non-exhaustive) list of topics:
? Tightening the relationship between theory and practice
? Applications of theory to explain empirical successes or failures
? Gaps where theory don’t explain practical successes or failures
? New, empirically inspired directions for theory
? Case studies where theory helped improve application of ML
or with general well argued positions on
? What kind of foundations contribute to practical success?
? The role of theory for interpreting empirical findings; Can an experiment be trusted without a theory?
? What is a (un)principled algorithm?
Notification of acceptance for short talk or poster is sent by the 4:th of October. Please state if you have a preference between oral or poster presentation.
Organizers:
? Peter Sunehag, the Australian National University peter.sunehag-AT-anu.edu.au
? Marcus Hutter, the Australian National University marcus.hutter-AT-anu.edu.au
? Mark Reid, the Australian National University mark.reid-AT-anu.edu.au