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I'm Joe Austerweil, a fourth-year psychology PhD student at UC Berkeley. My main advisor is Tom Griffiths in the Computational Cognitive Science lab exploring inductive inferences. I also work with Tania Lombrozo exploring computational accounts of explanation satisfaction (such as, probability, simplicity, and generality). I also also work with Steve Palmer on modeling color preferences and connecting work on representations in the conceptual and perceptual literatures.
My research explores the interconnection between human and statistical solutions to inductive problems. I am interested in using statistical models to garner insight to how the human mind solves problems that plague philosophers and computer scientists. By looking at the assumptions behind these computational models, we better understand the prior assumptions people use to make surprisingly accurate inferences in the underconstrained problems of everyday life. Additionally, I explore infusing these assumptions into state-of-the-art machine learning techniques. I hope to improve their performance on everyday tasks (where people, with surprisingly less data, easily outperform them).
Learning invariant features using the Transformed Indian Buffet Process
as author at Video Journal of Machine Learning Abstracts - Volume 1,