Seminars this semester


   Series:


Oct 12 Thu Dino Sejdinovic (Oxford)
14:00 LT 9 Approximate Kernel Embeddings and Symmetric Noise Invariance
 
  Abstract:
Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric hypothesis testing and for learning on distributional inputs. I will give an overview of this framework and present some of the applications of the approximate kernel embeddings to Bayesian computation. Further, I will discuss a recent modification of MMD which aims to encode invariance to additive symmetric noise and leads to learning on distributions robust to the distributional covariate shift, e.g. where measurement noise on the training data differs from that on the testing data. https://arxiv.org/abs/1703.07596
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Oct 19 Thu Mauricio Alvarez (Sheffield)
14:00
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Nov 9 Thu Arthur Gretton (UCL)
14:00
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Nov 16 Thu Timothy Waite (Manchester)
14:00
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Dec 7 Thu Maria Kalli (Kent)
14:00
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