Dino Sejdinovic - Associate Professor at the Department of Statistics, University of Oxford, Fellow of Mansfield College, Oxford, and Turing Fellow of the Alan Turing Institute. Data Science
Date: April 25th 2019 Location: Eurecom - Eurecom
Learning on Aggregate Outputs with Kernels. While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs. Aggregation of outputs makes generalization to new inputs much more difficult. We consider an approach to this problem based on variational learning with a model of output aggregation and Gaussian processes, where aggregation leads to intractability of the standard evidence lower bounds. We propose new bounds and tractable approximations, leading to improved prediction accuracy and scalability to large datasets, while explicitly taking uncertainty into account. We develop a framework which extends to several types of likelihoods, including the Poisson model for aggregated count data. We apply our framework to a challenging and important problem, the fine-scale spatial modelling of malaria incidences. Joint work with Leon Law, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, and Kenji Fukumizu. Biography: Dino Sejdinovic is an Associate Professor at the Department of Statistics, University of Oxford, a Fellow of Mansfield College, Oxford, and a Turing Fellow of the Alan Turing Institute. He previously held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Institute for Statistical Science, University of Bristol (2009-2011) and worked as a data science consultant in the financial services industry. He received a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006).