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<p class="MsoPlainText">Dear All,<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">We are pleased to invite you to our next Research Seminar.<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">Looking forward to seeing you online<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText"><span lang="FR-CH">Organizers :
<o:p></o:p></span></p>
<p class="MsoPlainText"><span lang="FR-CH">E. Cantoni - S. Engelke - D. La Vecchia - E. Ronchetti<o:p></o:p></span></p>
<p class="MsoPlainText">S. Sperlich - F. Trojani - M.-P. Victoria-Feser<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">FRIDAY, 15 MAY 2020 at 11:15am, Online: https://unige.zoom.us/j/873855655<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">Boosting Gaussian Process and Mixed Effects Models<o:p></o:p></p>
<p class="MsoPlainText">Prof. Fabio SIGRIST– Lucerne University of Applied Sciences and Arts<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">ABSTRACT:<o:p></o:p></p>
<p class="MsoPlainText">Boosting and Gaussian process models are widely used tools in applied machine learning and statistics. To date, these methods have been considered as separate approaches with each having its advantages. We propose a novel way to combine
boosting with Gaussian process and mixed effects models. This allows for relaxing (i) the linearity assumption for the mean function in Gaussian process and mixed effects models in a flexible non-parametric way and (ii) the independence assumption made in
most boosting algorithms. The former is advantageous for predictive accuracy and for avoiding model misspecifications. The latter is important for more efficient learning of the mean function and for obtaining probabilistic predictions. In addition, we present
an extension that scales to large data using a Vecchia approximation for the Gaussian process model relying on novel results for covariance parameter inference. We obtain increased predictive performance compared to existing approaches using several simulated
datasets and in house price and online transaction applications.<o:p></o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText"><o:p> </o:p></p>
<p class="MsoPlainText">Visit the website: https://www.unige.ch/gsem/en/research/seminars/rcs/<o:p></o:p></p>
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