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Hassan Z. Ashtiani gets Best Paper AwardDecember 3, 2018

Best paper award in the Neural Information Processing Systems (NeurIPS) conference

NeurIPS (previously called NIPS) is the flagship venue in machine learning. The award-winning paper, titled

"Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes"

establishes fundamental connections between distribution learning and compression.  It was a joint work between his colleagues across Canadian universities. This year, four papers received this award among a record-breaking number of 4856 submissions.

Here is the link to the paper:

http://papers.nips.cc/paper/7601-nearly-tight-sample-complexity-bounds-for-learning-mixtures-of-gaussians-via-sample-compression-schemes

Well done Hassan!