May 01, 2019

Per-sample Prediction Intervals for Extreme Learning Machines

  • Akusok A.
  • Bjork K.
  • Lendasse A.
  • Miche Y.

Prediction Intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary for separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and for other problem-specific tasks in applied Machine Learning. Stochastic projection functions ii many real problems do not  correspond to homoscedastic assumption of the noise in a dataset, and the input-independent variance of noise computed by Mean Squared Error performs poorly in such cases. The paper proposes to use the weighted Jackknife estimator of the output weights variance, and a methodology to compute input-specific Prediction Intervals from that variance. The key features of the proposed methodology are robustness to heteroscedastic noise, standard formulation of ELM, short runtime and feasibility for large datasets.

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Recent Publications

January 01, 2019

Friendly, appealing or both? Characterising user experience in sponsored search landing pages

  • Bron M.
  • Chute M.
  • Evans H.
  • Lalmas M.
  • Redi M.
  • Silvestri F.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, ...

January 01, 2019

Analyzing uber's ride-sharing economy

  • Aiello L.
  • Djuric N.
  • Grbovic M.
  • Kooti F.
  • Lerman K.
  • Radosavljevic V.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Uber is a popular ride-sharing application that matches people who need a ride (or riders) with drivers who are willing to provide it using their personal vehicles. Despite its growing popularity, there exist ...

January 01, 2019

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race

  • Cresci S.
  • Petrocchi M.
  • Pietro R.
  • Spognardi A.
  • Tesconi M.

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel ...