We read papers covering topics related to techniques for statistical inference and machine learning (including deep learning). Feel free to email me at heejung.shim@unimelb.edu.au if you'd like to join for discussion or you have any paper suggestions

• We meet fortnightly on Monday at 4pm via zoom unless otherwise stated.

Date	Paper	Discussion leader
May 26 2021	Variational Learning of Inducing Variables in Sparse Gaussian Processes, Michalis Titsias, 2009
April 26 2021	Bayesian Gaussian Process Latent Variable Model, Michalis Titsias and Neil Lawrence, 2010
March 29 2021	Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models, Neil Lawrence, 2005
March 15 2021	The mathematics of UMAP, Adele Jackson, 2019
March 2 2021	Diffusion maps, Coifman and Lafon, 2006
Feb 9 2021	A Global Geometric Framework for Nonlinear Dimensionality Reduction, Tenenbaum et al, 2000
January 12 2021	Neural Ordinary Differential Equations, Chen et al, 2018
December 15 2020	Variational Boosting: Iteratively Refining Posterior Approximations, Miller et al, 2017
December 1 2020	Automatic Differentiation in Machine Learning: a Survey, Baydin et al, 2018
November 17 2020	Covariances, Robustness, and Variational Bayes, Giordano et al, 2018	Martin?
November 3 2020	Chapter 4.3 Expectation-Propagation Algorithms of Graphical Models, Exponential Families, and Variational Inference, Wainwright and Jordan, 2008
October 20 2020	Chapter 4.1 Sum-Product and Bethe Approximation of Graphical Models, Exponential Families, and Variational Inference, Wainwright and Jordan, 2008
October 6 2020	Chapter 3 Graphical Models as Exponential Families of Graphical Models, Exponential Families, and Variational Inference, Wainwright and Jordan, 2008
September 22 2020	Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Hasenclever et al, 2017
September 7 2020	Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data, Vehtari et al, 2019
August 24 2020	Expectation Propagation for Approximate Bayesian Inference, Thomas P Minka, 2001	Martin
August 10 2020	VAE with a VampPrior, Tomczak and Welling, 2018	Yong See
July 27 2020	BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders, Märtens and Yau, 2020	Heejung
July 13 2020	Composing graphical models with neural networks for structured representations and fast inference, Johnson et al, 2016	Martin
July 6 2020	A Unifying Review of Linear Gaussian Models, Roweis and Ghahramani, 1999
June 15, 29 2020	Particle Markov chain Monte Carlo methods, Andrieu et al, 2010	Heejung/Martin
	An Introduction to Sequential Monte Carlo Methods, Doucet et al, 2001
June 9 2020	Bayesian posterior sampling via stochastic gradient fisher scoring, Ahn et al, 2012
May 25 2020	Bayesian Computing with INLA: A Review, Rue et al, 2017	Martin
May 18 2020	A Complete Recipe for Stochastic Gradient MCMC, Ma et al, 2015	Heejung
May 11 2020	Bayesian Learning via Stochastic Gradient Langevin Dynamics, Welling and Teh, 2011	Heejung
May 4 2020	Unbiased Implicit Variational Inference, Titsias and Ruiz, 2019
April 27 2020	Stochastic Gradient Descent as Approximate Bayesian Inference, Mandt et al, 2017	Martin
April 20 2020	A Survey of Optimization Methods from a Machine Learning Perspective, Sun et al, 2019
April 14 2020	An overview of gradient descent optimization algorithms, Sebastian Ruder, 2017 and/or this blog	Qiuyi
March 30 2020	Tutorial on Variational Autoencoders, CARL DOERSCH, 2016 and What is a variational autoencoder?	Hui
March 9 2020	Automatic Differentiation Variational Inference, Kucukelbir et al, 2017	Anubhav
March 4 and 9 2020	Martin's presentation: Scalable Multi-output Gaussian Processes	Martin
March 2 2020	Black Box Variational Inference, Ranganath et al, 2014 and Video: presentation by David Blei
Feb 17 2020	The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, Hoffman and Gelman, 2014
Feb 3 2020	MCMC using Hamiltonian dynamics, Radford M. Neal, 2012 and Video on HMC
Jan 20 2020	Scaling probabilistic models of genetic variation to millions of humans, Gopalan et al, 2016
Jan 13 2020	Stochastic Variational Inference, Hoffman et al, 2013
Dec 13 2019	Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis, Engelhardt and Stephens, 2010
Nov 5 and 20 2019	fastSTRUCTURE: Variational Inference of Population fStructure in Large SNP Data Sets, Raj et al, 2014
Oct 30 2019	Inference of Population Structure Using Multilocus Genotype Data, Pritchard et al, 2000
Oct 15 2019	Empirical Bayes Matrix Factorization, Wang and Stephens, 2018
Oct 8 2019	Variational Inference: A Review for Statisticians, Blei et al, 2017 - Implementation of VI algorithm
Sept 17 2019	Variational Inference: A Review for Statisticians, Blei et al, 2017 - Derivation of VI algorithm
Aug 27 2019	Variational Inference: A Review for Statisticians, Blei et al, 2017