Probabilistic Data Structures and Algorithms

Add to your list(s) Download to your calendar using vCal

• Christian Steinruecken (University of Cambridge), Alexandre Khae Wu Navarro (University of Cambridge)
• Thursday 23 January 2014, 15:00-16:30
• Engineering Department, CBL Room 438.

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.

This talk is included in these lists:

• All Cavendish Laboratory Seminars
• All Talks (aka the CURE list)
• Biology
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge Neuroscience Seminars
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Health Leadership and Enterprise
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Engineering Department, CBL Room 438
• Featured lists
• Guy Emerson's list
• Hanchen DaDaDash
• Inference Group
• Inference Group Journal Clubs
• Inference Group Summary
• Information Engineering Division seminar list
• Interested Talks
• Joint Machine Learning Seminars
• Life Science
• Life Sciences
• ME Seminar
• ML
• Machine Learning Reading Group
• Machine Learning Reading Group @ CUED
• Machine Learning Summary
• Neurons, Fake News, DNA and your iPhone: The Mathematics of Information
• Neuroscience
• Neuroscience Seminars
• Neuroscience Seminars
• Quantum Matter Journal Club
• Required lists for MLG
• School of Physical Sciences
• School of Technology
• Simon Baker's List
• Stem Cells & Regenerative Medicine
• TQS Journal Clubs
• Thin Film Magnetic Talks
• Trust & Technology Initiative - interesting events
• bld31
• custom
• dh539
• kt532's list
• ndk22's list
• ob366-ai4er
• rp587
• yk373's list

Note that ex-directory lists are not shown.