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Probabilistic Data Structures and Algorithms

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If you have a question about this talk, please contact Konstantina Palla .

Classic software engineering encourages us to think of a computer as a perfect machine that has an error probability of zero. Software components are typically designed to assume and provide exactly this type of perfection. Amazingly, it is possible to construct powerful and efficient algorithms by relaxing the zero error constraint: the demand for space and time resources can be drastically reduced in exchange for accepting a small, non-zero probability of error. This RCC shows a variety of such techniques, probabilistic data structures and algorithms, and how they can be used for machine learning on massive datasets.

Required reading: none. Instead, please think about the following question: “What can be gained from randomness? Can randomness ever help us solve deterministic problems? (And if so, how?)”

This talk is part of the Machine Learning Reading Group @ CUED series.

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