Knowledge transfer using latent variable models
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In several applications, scarcity of labeled data is a challenging problem that hinders the predictive capabilities of machine learning algorithms. Additionally, the distribution of the data changes over time, rendering models trained with older data less capable of discovering useful structure from the newly available data. Transfer learning is a convenient framework to overcome such problems where the learning of a model specific to a domain can benefit the learning of other models in other domains through either simultaneous training of domains or sequential transfer of knowledge from one domain to the others. This thesis explores the opportunities of knowledge transfer in the context of a few applications pertaining to object recognition from images, text analysis, network modeling and recommender systems, using probabilistic latent variable models as building blocks. Both simultaneous and sequential knowledge transfer are achieved through the latent variables, either by sharing these across multiple related domains (for simultaneous learning) or by adapting their distributions to fit data from a new domain (for sequential learning).