Seminar: Distribution Regression  the Set Kernel Heuristic is Consistent
Speaker  Zoltan Szabo 

Affiliation  UCL, Gatsby 
Date  Friday, 02 May 2014 
Time  13:00  14:00 
Location  Malet Place Engineering 1.03 
Event series  DeepMind CSML Seminar Series 
Description 
Bag of feature (BoF) representations are omnipresent in machine learning; for example, an image can be described by a bag of visual features, a document might be considered as a bag of words, or a molecule can be handled as a bag of its different configurations. Set kernels (also called multiinstance or ensemble kernels; Gaertner 2002) defining the similarity of two bags as the average pairwise point similarities between the sets, are among the most widely applied tools to handle problems based on such BoF representations. Despite the wide applicability of set kernels, even the most fundamental theoretical questions such as their consistency in specific learning tasks is unknown. In my talk, I am going to focus on the distribution regression problem: regressing from a probability distribution to a realvalued response. By considering the mean embeddings of the distributions, this is a natural generalization of set kernels to the infinite sample limit: the bags can be seen as i.i.d. (independent identically distributed) samples from a distribution. We will propose an algorithmically simple ridge regression based solution for distribution regression and prove its consistency under fairly mild conditions (for probability distributions defined on locally compact Polish spaces). As a special case, we give positive answer to a 12yearold open question, the consistency of set kernels in regression. We demonstrate the efficiency of the studied ridge regression technique on (i) supervised entropy learning, and (ii) aerosol prediction based on satellite images. [preprint, code]

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