Machine Learning Coffee seminar: "Does my algorithm work?" Daniel Simpson, University of Toronto

Weekly seminars held jointly by Aalto University and the University of Helsinki.

Helsinki region machine learning researchers will start our week by an exciting machine learning talk. The aim is to gather people from different fields of science with interest in machine learning. Porridge and coffee is served at 9:00 and the talk will begin at 9:15. The venue for this talk is seminar room T5, CS building.

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Does my algorithm work?

Daniel Simpson
Professor of Statistical Sciences, University of Toronto

Abstract:

It is easy to propose a new algorithm for solving a Machine Learning problem. It is much harder to convince other people that the proposed algorithm actually works. The "gold standard" of tight theoretical guarantees is often out of reach. So what do we do? Typically, an algorithm is validated on a couple of test problems and its output is compared with that of algorithms that are known to work. This is not a great strategy.

In this talk, I will outline a general strategy for assessing whether an algorithm for approximate Bayesian computing works on a given problem. This method does not require evaluation of the true posterior and also indicates ways in which the computed posterior systematically deviates from the true posterior.

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See the next talks at the seminar webpage.

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