CS Professor Nick Harvey & PhD Student Christopher Liaw win NeurIPS 2018 Best Paper Award

The paper, entitled "Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes", was awarded a NeurIPS 2018 Best Paper Award, 1 of 4 awards among 4854 submissions.  It was presented at the NeurIPS 2018 conference in Montreal, a flagship machine learning conference.

The paper is authored by Hassan Ashtiani (McMaster), Shai Ben-David (Waterloo), Nicholas Harvey (UBC CS), Christopher Liaw (UBC CS), Abbas Mehrabian (McGill, formerly a UBC postdoc), and Yaniv Plan (UBC Math). The paper gives nearly-tight sample complexity bounds for learning a mixture of Gaussians in a distribution learning setting.

Congratulations to Professor Nicholas Harvey and PhD student Christopher Liaw!

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