Intuidex - To Be or Not To Be IID

In this talk William M. Pottenger of Intuidex will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners. This talk was recorded at NYC Machine Learning meetup at Pivotal Labs.

Much prior work has shown the practical value of modeling random variables as IID in order to simplify statistical inference, yet prior work has also shown this assumption to be suboptimal in terms of model performance. Occam’s razor prompts us to simplify explanations, and this talk will present how a very simple transform has been leveraged to improve performance of both generative and discriminative learners, as well as unsupervised learning, in a number of application domains including differentially private community discovery.

 



William M. Pottenger is an Associate Research Professor at Rutgers University at DIMACS and RUTCOR in the field of Computer Science. Bill is also Director of Transition for CCICADA, the DHS Command, Control and Interoperability Center for Advanced Data Analysis. He is also founder of Intuidex, a manufacturer of solutions in the visual and data analytics space.

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