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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Winter term 1 2019 - Why Deep Learning WorksEvery Wednesday in room ICICS X836 at 1:00 PM | ||

Date | Presenter | Topic |

Sep 25 | Aaron | Motivation - [pdf slides] |

Oct 2 | Jason | Generalization of Neural Networks (arXiv ID: 1611.03530) - [pdf slides] |

Oct 9 | Amir | Sharp Minima Generalize Poorly (arXiv ID: 1609.04836) - [pdf slides] |

Oct 16 | Adam | Sharp Minima Can Generalize Well (arXiv ID: 1703.04933) - [pdf slides] |

Oct 23 | Will | Capacity Measures (arXiv ID: 1706.08947) - [pdf slides] |

Oct 30 | Cathy | Implicit Regularization of Optimizers (arXiv ID: 1705.03071) - [pdf slides] |

Nov 6 | Betty | Implicit Bias of SGD: Matrix Factorization (arXiv ID: 1705.09280) - [pdf slides] |

Nov 13 | Fred | Implicit Bias of SGD: Logistic Regression (arXiv ID: 1710.10345) - [pdf slides] |

Nov 20 | Joey | Generalization of Over-Parameterized Kernels (arXiv ID: 1802.01396) - [pdf slides] |

Nov 27 | Wilder | Over-Parameterization and Bias-Variance (arXiv ID: 1812.11118) - [pdf slides] |

Dec 4 | Alireza | Big Architectures and Overfitting the Test Set (arXiv ID: 1902.10811) - [pdf slides] |

Dec 11 | Ben | Over-Parameterization: Generalization Bounds (arXiv ID: 1805.12076) - [pdf slides] |

Summer term 2019 - Online LearningEvery Wednesday in room ICICS 146 at 1:00 PM | ||

Date | Presenter | Topic |

Jun 19 | Yihan | Introduction to Online Learning - [pdf slides] |

Jun 26 | Cathy | Multiplicative Weight Update - [pdf slides] |

Jul 3 | Amit | Follow the Leader - [pdf slides] |

Jul 10 | Chris | Introduction to Bandits - [pdf slides] |

Jul 17 | Sikander | Contextual Bandits - [pdf slides] |

Jul 24 | Jason | Thompson Sampling - [pdf slides] |

Jul 31 | Manyou | Markovian Bandits - [pdf slides] |

Aug 14 | Lironne | Dueling Bandits |

Aug 21 | Yihan | Linear Bandits |

Winter term 2 2019 - Representation LearningEvery Monday in room ICICS 146 at 5:00 PM | ||

Date | Presenter | Topic |

Feb 4 | Yifan | Introduction to Representation Learning - [pdf slides] |

Feb 11 | Amir | Artistic Style Transfer |

Feb 18 | (Holiday) | |

Feb 25 | Aaron | GANS |

Mar 4 | Wilder | Manifold Learning |

Mar 11 | Cathy | Convolutional Graph Embeddings - [pdf slides] |

Mar 18 | Jason | Variational Autoencoders |

Mar 25 | Marjan | Graph and Point Cloud Embeddings |

Apr 1 | Michael | Disentanglement |

Apr 8 | Canceled | |

Apr 15 | Yihan | Dictionary Learning - [pdf slides] |

Winter term 1 2018 - Reinforcement Learning 2Every Monday in room ICICS 146 at 5:00 PM | ||

Date | Presenter | Topic |

Oct 15 | Mark | Motivation/Overview - [pdf slides] |

Oct 22 | Yifan | Bayesian RL - [pdf slides] |

Oct 29 | Christian | Useful Uncertainties in Reinforcement Learning - [pdf slides] |

Nov 5 | Sharan | Introduction to Bandits - [pdf slides] |

Nov 12 | Cancelled | |

Nov 19 | Aaron | Policy Gradient Algorithms - [pdf slides] |

Nov 26 | Boyan | Verification of NN Properties: Examples from Supervised and Reinforcement Learning |

Dec 3 | Wilder | Methods and Applications for Inverse Reinforcement Learning |

Dec 10 | Mehrdad | Introduction to Contextual Bandits |

Dec 17 | Vaden | Introduction to Bayesian Non-Parametrics |

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] |