UBC Machine Learning Reading Group (MLRG)

Summer term 2018


The Machine Learning Reading Group (MLRG) meets regularly (usually weekly) to discuss research topics on a particular sub-field of Machine Learning.


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Reading group schedules

Summer 2018 - Every Tuesday in room ICICS 146 at 3:00 PM
Date Presenter Topic
May 08 Emtiyaz Khan Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
May 15 Geoff Roeder Better Inference through Lower-Variance Stochastic Gradients
May 22 Brendan Juba Learning Abduction Under Partial Observability

Winter term 2 2018 - Parallel and Distributed Machine LearningEvery Tuesday in room ICICS 146 at 5:00 PM
Date Presenter Topic
Jan 30 Mark Schmidt Motivation - [pdf slides]
Feb 6 Yasha Distributed file systems
Feb 13 Michael Asynchronous stochastic gradient
Feb 27 Sharan Synchronous stochastic gradient - [pdf slides]
Mar 6 Julie Parallel coordinate optimization - [pdf slides]
Mar 13 Devon Decentralized gradient
Mar 20 Wu Decomposition methods
Mar 27 Reza Asynchronous/distributed SAG/SDCA/SVRG
Apr 3 Vaden Randomized Newton and least squares on the cloud
Apr 10 Nasim Parallel tempering and distributed particle filtering
Apr 17 Alireza Distributed deep networks
Apr 24 Raunak Blockchain-based distributed learning

Winter term 1 2017 - Deep Learning Meets Graphical ModelsEvery Tuesday in room ICICS 146 at 4:00 PM
Date Presenter Topic
Sep 26 Mark Motivation/overview - [pdf slides]
Oct 3 Issam FCNs and CRFs
Oct 10 Julieta RNNs
Oct 17 Michael Bayesian neural nets 1: sampling
Oct 24 Jason Bayesian neural nets 2: variational
Oct 31 Devon Variational autoencoders 1: basics/ - [pdf slides]
Nov 7 Sharan Variational autoencoders 2: variations - [pdf slides]
Nov 14 Mohamed Generative adversarial networks 1: basics
Nov 21 Alireza Generative adversarial networks 2: variations
Nov 28 Raunak Beyond generative adversarial networks/ - [pdf slides]

Summer 2017 - Online, Active, and Causal learningEvery Tuesday in room ICICS 146 at 4:00 PM,
Date Presenter Topic,,,
Jun 6 Mark Schmidt Motivation/overview, perceptron, follow the leader. - [pdf slides]
Jun 13 Julie Online convex optimization, mirror descent - [pdf slides]
Jun 20 Alireza Multi-armed bandits, contextual bandits - [pdf slides]
Jun 27 Michael Heavy hitters,,,
Jul 4 Raunak Regularized FTL, AdaGrad, Adam, online-to-batch - [pdf slides]
Jul 11 Glen Best-arm identification, dueling bandits,,
Jul 18 Nasim Uncertainty sampling, variance/error reduction, QBC - [pdf slides]
Jul 25 Mohamed Planning, A/B testing, Optimal experimental design,
Aug 1 Sanna Randomized controlled trials, do-calculus - [pdf slides]
Aug 8 Issam Granger causality, independent component analysis,,
Aug 22 Eric Counterfactuals - [pdf slides]
Aug 29 Jason Instrumental variables,,,

Winter term 2 2017 - Reinforcement LearningEvery Tuesday in room ICICS 146 at 5:00 PM,,
Date Presenter Topic,,
Jan 10 Mark Schmidt Motivation/Overview - [pdf slides]
Jan 17 Nasim MDPs (policy iteration, value iteration),
Jan 24 Julie Monte Carlo (estimators, on-policy/off-policy learning) - [pdf slides]
Jan 31 Raunak Temporal Difference Learning,,
Feb 7 Jennifer Multi-Step Bootstrapping/ - [pdf slides]
Feb 14 Michael Function Approximation, TD-Gammon,
Feb 21 Cancelled,,
Feb 28 Ricky Planning, Control with Approximation, and Eligibility Traces
Mar 7 Issam Optimal control, flying helicopters,
Mar 14 Sharan POMDPs - [pdf slides]
Mar 21 Jason Policy gradients, Monte Carlo tree search, and AlphaGo
Mar 28 Julieta Value-Iteration Networks,,
Apr 4 Glen RL in Practice,,
Apr 11 Michiel Perspectives on Reinforcement Learning for Locomotion Skills,,
Apr 25 Issam Connection between Generative Adversarial Networks and Inverse Reinforcement Learning,,

Winter term 1 2016 - Deep LearningEvery Wednesday in room ICICS 146 at 5:00 PM
Date Presenter Topic
Sep 21 Mark Schmidt Introduction - [pdf slides]
Sep 28 Julie Feedforward neural nets, backpropagation - [pdf slides]
Oct 5 Mohamed Network-independent tricks - [pdf slides]
Oct 12 Issam ImageNet tricks
Oct 19 Jason Graphical models - [pdf slides]
Oct 26 Saif Artistic style transfer - [pdf slides]
Nov 2 Nasim Recurrent neural nets - [pdf slides]
Nov 9 Stephen/Kevin Recurrent neural nets 2
Nov 16 Ricky Variational autoencoders and Bayesian dark knowledge
Nov 23 Reza Generative adversarial networks
Nov 30 Alireza Memory nets, neural Turing, stack-augmented RNNs

Summer term 2016 - MiscellaneousEvery Wednesday in room ICCS146 at 5:00 PM
Date Presenter Topic
May 25 Mark Schmidt Introduction to Summer topics - [pdf slides]
Jun 1 No meeting UAI camera-ready deadline
Jun 8 Sharan Spectral Methods (1) - [pdf slides]
Jun 15 Geoff Spectral Methods (2) - [pdf slides]
Jun 22 Chris Relational Models
Jun 29 Saif Submodularity - [pdf slides]
Jul 6 Nasim Grammars - [pdf slides]
Jul 13 Eviatar Continuous graphical models - [pdf slides]
Jul 20 Steven and Kevin Gaussian Copulas - [pdf slides]
Jul 27 Issam Large-scale kernels methods (1)
Aug 3 Julietta Large-scale kernels methods (2)
Aug 10 Alireza Changepoint detection (1)
Aug 17 Mohamed Changepoint detection (2)
Aug 24 Julie Independent component analysis (1)
Aug 31 Ricky Independent component analysis (2)

Winter term 2 2016 - Crash course on Bayesian methodsEvery Wednesday in room ICICS 146 at 5:00 PM
Date Presenter Topic
Jan 06 Mark Schmidt Introduction to Bayesian methods - [pdf slides]
Jan 13 Nasim Conjugate Priors, Non-Informative Priors - [pdf slides]
Jan 20 Geoff Hierarchical Modeling and Bayesian Model Selection - [pdf slides]
Jan 27 Issam Gaussian Processes and Empirical Bayes - [pdf slides]
Feb 3 Ricky Basic Monte Carlo Methods - [pdf slides]
Feb 10 Jason MCMC - [website link]
Feb 24 Michael Bayesian Optimization - [pdf slides]
Mar 2 Sharan Variational Bayes - [pdf slides]
Mar 9 Reza Stochastic Variational Inference - [pdf slides]
Mar 16 Mark Non-Parametric Bayes 1 - [pdf slides]
Mar 23 Reza Non-Parametric Bayes 2
Apr 6 Julieta Sequential Monte Carlo and Population MCMC
Apr 13 Rudy Reversible-Jump MCMC
Apr 20 Alireza Approximate Bayesian Computation - [pdf slides]

Winter term 1 2015 - Crash course on optimizationEvery Tuesday in room X836 at 5:00 PM
Date Presenter Topic
Sep 22 Mark Schmidt Introduction to convex optimization - [pdf slides]
Sep 29 Mark Schmidt First-Order Methods - [pdf slides]
Oct 06 Julieta Stochastic Subgradient - [pdf slides]
Oct 13 Mohamed Minimizing Finite Sums - [pdf slides]
Oct 20 Jason Proximal-Gradient - [pdf slides]
Oct 27 Ives Frank-Wolfe, ADMM - [pdf slides]
Nov 03 Julie Coordinate Descent - [pdf slides]
Nov 10 Sharan Online Convex Optimization - [pdf slides]
Nov 17 Mark Schmidt Multi-Level Methods - [pdf slides]
Nov 24 Issam Non-Convex Rates - [pdf slides]
Dec 01 Issam Parallel/Distributed - [pdf slides]
Dec 08 (NIPS)
Dec 15 Alireza Deep Learning Local Optima - [pdf slides]

Summer term 2 2015 - Crash course on graphical modelsRoom ICICS 238 at 11:00 AM
Date Presenter Topic
Aug 17 Mark Schmidt Why learn about graphical models? - [pdf slides]
Aug 18 Mark Schmidt Inference in Chains and Trees - [pdf slides]
Aug 19 Julie Conditional Inference and Cutset Conditioning - [pdf slides]
Aug 20 Mehran Junction Tree - [pdf slides]
Aug 21 Alireza Semi-Markov/Graph Cuts - [pdf slides]
Aug 24 Mark Schmidt MRF/CRF - [pdf slides]
Aug 25 Julieta ICM/Block/Alpha - [pdf slides]
Aug 26 Jason MCMC/Herding - [pdf slides]
Aug 27 Ankur Hidden/RBM/Younes - [pdf slides]
Aug 28 Sharan Structure Learning - [pdf slides]
Aug 31 Mark Schmidt Variational/MF - [pdf slides]
Sep 1 Nasim Bethe/Kikuchi - [pdf slides]
Sep 2 Reza TRBP/Convex - [pdf slides]
Sep 3 Issam LP/SDP - [pdf slides]
Sep 4 Mark Schmidt SSVM/BCFW - [pdf slides]