​​UNSW Computational Statistics / Machine Learning  Reading Group
​​Each session ,the person leading the session will prescribe some reading (to be read beforehand), and during the session will lead a discussion about the material. 
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​​Due to the current situation, these sessions are on hold indefinitely.

​​Future Sessions 

​​Theme: Optimal Transport

​​Why are we looking at this stuff?  See this brief Discussion Paper on Applications in Methodological Statistics/Machine Learning  (we will look at some of the things mentioned in Part II).

​​Part I: Foundations

​​Friday 13th March, 11AM — Rob Salomone
​​Location: Business School 130
​​Reading: pp. 1-30 of the Book (resp. pp. 1–15 of Course Notes)
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​​Topic: Entropic Regularization & Sinkhorn Iteration 
​​Topic: Wasserstein Barycenters & Wasserstein Estimation (Xuhui) 
​​Topic: Gradient Flows (Spiro)
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​​Gery Geenens
​​Yu Guang Wang
​​Yu Yang
​​Matt Gibson
​​Dan Mackinlay
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​​Part II: Methodological/Related Papers

​​Resources 

​​Extended Slides
​​Book - Computational Optimal Transport
​​Course Notes 
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​​Past Sessions

​​Theme: Variational Autoencoders (and Related Topics)

​​17th September, 2019 — Rob Salomone 
​​Auto-Encoding Variational Bayes
​​ 2nd October, 2019 — Boris Beranger
​​Towards a Neural Statistician
​​16th October, 2019 — Xuhui Fan
​​Hamiltonian Variational Autoencoder
​​  23rd October, 2019 — Dan Mackinlay
​​Variational Inference with Normalizing Flows / Sylvester Flows 
​​6th November, 2019 —  Yu Guang Wang
​​Neural ODEs / Augmented Neural ODEs