The UBC 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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Upcoming Talks
Fall 2022 – Transformers
Every Wednesday at 1:00PM in ICCS 146. A Zoom link will be provided in the weekly reminder email for those wishing to attend remotely.
Past Sessions
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| Sept. 28 | Dylan | Introduction | Slides |
| Oct. 5 | Alan | Attention is All You Need https://arxiv.org/abs/1706.03762 | Slides |
| Oct. 12 | Helen | Language Models are Few Shot Learners (GPT-3) https://arxiv.org/abs/2005.14165 | Slides |
| Oct. 19 | ~ | Postponed | … |
| Oct. 26 | Curtis | An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale https://arxiv.org/abs/2010.11929 | Slides |
| Nov. 2 | Mehar | Chain of Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903 | Slides |
| Nov. 9 | ~ | READING WEEK | … |
| Nov. 16 | ~ | Rescheduled | … |
| Nov. 23 | ~ | Cancelled | … |
| Nov. 30 | Issam | Learning Transferable Visual Models From Natural Language Supervision (CLIP) https://arxiv.org/abs/2103.00020 | Slides |
| Dec. 7 | Adam | Hierarchical Text-Conditional Image Generation with CLIP Latents https://arxiv.org/abs/2204.06125 | Slides |
| Dec. 14 | Betty | Neural Scaling Laws + Exploring the Limits of Large Scale Pre-Training https://arxiv.org/abs/2001.08361, https://arxiv.org/abs/2110.02095 | Slides |
Summer 2022 – Random Topics
Every Wednesday at 1:00 PM Online or In-Person (ICCS 238)
| Date | Presenter | Topic and Paper | Slides | In-Person or Online |
|---|---|---|---|---|
| June 1 | Issam | Build End-to-End Machine Learning Projects for Large-Scale Optimization Experiments | Slides Colab | In-Person |
| June 8 | Wu | The Riemannian Geometry of Deep Generative Models https://arxiv.org/pdf/1711.08014.pdf | Slides | In-Person |
| June 15 | Betty | Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling https://arxiv.org/abs/2112.15199 | Slides | In-Person |
| June 22 | Helen | Active Learning in Semantic Image Segmentation Paper Paper2 | Slides | In-Person |
| June 29 | Alan | A-NICE-MC: Adversarial Training for MCMC https://arxiv.org/abs/1706.07561 | Slides | In-Person |
| July 6 | Curtis | Adaptive Gradient Descent without Descent https://arxiv.org/abs/1910.09529 | In-Person | |
| July 13 | Nick | Not All Samples Are Created Equal: Deep Learning with Importance Sampling https://arxiv.org/pdf/1803.00942.pdf | In-Person | |
| July 20 | Amrutha | Assessing Generalization via Disagreement https://arxiv.org/pdf/2106.13799.pdf | In-Person | |
| July 27 | Bahareh | Relational Multi-Task Learning: Modeling Relations between Data and Tasks https://openreview.net/pdf?id=8Py-W8lSUgy | In-Person | |
| August 10 | Dylan | Diffusion and Score-Based Generative Models Paper1 Paper2 Paper3 | In-Person |
Winter 2021 term 2 – Random Topics
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| February 16 | Wilder | Gradients are Not All You Need https://arxiv.org/abs/2111.05803 | … |
| March 2 | Helen | Oops I Took a Gradient https://arxiv.org/abs/2102.04509 | Slides |
| March 9 | Amin | Neural Tangent Kernels https://arxiv.org/abs/1806.07572 | Slides |
| March 16 | Fred | Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability https://arxiv.org/abs/2103.00065 | Slides |
| March 23 | Betty | Quasi-Newton Methods for Machine Learning: Forget the Past, Just Sample https://arxiv.org/abs/1901.09997 | Slides |
| March 30 | Amrutha | Gradient Starvation: A Learning Proclivity in Neural Networks https://arxiv.org/abs/2011.09468 | Slides |
| April 6 | Dylan | Do Deep Generative Models Know What They Don’t Know? https://arxiv.org/abs/1810.09136 | Slides |
| April 13 | Wu | Monte Carlo Gradient Estimation in Machine Learning https://arxiv.org/abs/1906.10652 | Slides |
| April 20 | Bahareh | Inductive Representation Learning on Temporal Graphs https://arxiv.org/abs/2011.09468 | Slides |
| April 27 | Curtis | Super-Acceleration with Cyclical Step-sizes https://arxiv.org/abs/2106.09687 | Slides Video |
Winter 2021 term 1 – Responsible ML
Every Wednesday at 1:00 PM Online
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| Oct 13 | Lironne | Introduction | Slides |
| Oct 20 | Amrutha | DeepFake Detection | Slides |
| Oct 27 | Namrata | Fairness without Demographics | Slides |
| Nov 3 | Wilder | Ethics and Governance of Artificial Intelligence | Slides |
| Nov 10 | Yasha | Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias | Slides |
| Nov 17 | Fred | Lessons from Archives Strategies for Collecting Sociocoltural Data | Slides |
| Nov 24 | Betty | Optimizing Long-term Social Welfare in Recommender Systems | Slides |
| Dec 1 | Dylan | On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? | Slides |
| Dec 8 | Helen | Understanding The Origins of Bias in Word Embeddings | Slides |
Summer 2021 – Geometric structures in machine learning
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| Jul 7 | Wu | Introduction | Slides |
| Jul 14 | Nick | Basics of Geometric Deep Learning | Slides |
| Jul 21 | Wu | Basics of manifolds | Slides |
| Jul 28 | Fred | Riemannian optimization | Slides |
| Aug 4 | Christian | Natural Wake-Sleep Algorithm | |
| Aug 11 | Victor | Riemannian optimization | |
| Aug 18 | Wilder | Natural Gradient Boosting for Probabilistic Prediction | |
| Aug 25 | Betty | Predicting anticancer hyperfoods with graph convolutional networks | Slides |
| Sep 1 | Wu | (bonus) Basics of information geometry and matrix groups |
Winter term 2 2020 – Modern Deep Learning Architectures
Every Wednesday at 1:00 PM Online
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| Jan 27 | Fred | Introduction | Slides |
| Feb 3 | Victor | Computer Vision: Deep models for segmentation He et al. (2018), Mask R-CNN, | Slides |
| Feb 10 | Adam | Computer Vision: 3D point clouds Zhou Tuzel (2018), VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | Slides |
| Feb 17 | Mark | Natural Language Processing: Attention models Vaswani et al. (2017, Attention is All you Need | Slides |
| Feb 24 | Jacques | Sound and audio data: Text-to-speach Oord et al. (2016), WaveNet: A Generative Model for Raw Audio | Slides |
| Mar 3 | Betty | Batch Normalization Santurkar et al. (2018), How Does Batch Normalization Help Optimization? | Slides |
| Mar 10 | Danica | Generative Models: GANs and optimal transport Arjovsky et al. (2017), Wasserstein Generative Adversarial Networks | Slides |
| Mar 17 | Dylan | Generative Models: Autoregressive models and VAEs Chen et al. (2017), Variational Lossy Autoencoder | Slides |
| Mar 24 | Wilder | Neural ODE Chen et al. (2018), Neural Ordinary Differential Equations | Slides |
| Mar 31 | Lironne | Graph Neural Networks Kipf and Welling (2016), Semi-Supervised Classification with Graph Convolutional Networks | Slides |
Winter term 1 2020 – Tensor basics and applications
Every Wednesday at 1:00 PM Online
| Date | Presenter | Topic and Paper | Slides |
|---|---|---|---|
| Sep 30 | Betty | Motivation | Slides |
| Oct 7 | Mark | Tensor basics: Notation, operations, etc. (Kolda and Bader 2009) http://www.kolda.net/publication/TensorReview.pdf | Slides |
| Oct 14 | Betty | Tensor basics: Complexity (Hillar and Lim 2013) https://arxiv.org/abs/0911.1393 | Slides |
| Oct 21 | Bahare | Tensor factorization: Knowledge graphs (Kazemi and Poole 2018) https://papers.nips.cc/paper/7682-simple-embedding-for-link-prediction-in-knowledge-graphs.pdf | Slides |
| Oct 28 | Wilder | Latent models: Gaussian mixture models (Hsu and Kakade 2013) https://arxiv.org/pdf/1206.5766.pdf | Slides |
| Nov 4 | Emmanuel | Latent models: Topic models (Anandkumar et al. 2014) https://papers.nips.cc/paper/4637-a-spectral-algorithm-for-latent-dirichlet-allocation | Slides |
| Nov 11 | Remembrance Day | ||
| Nov 18 | Fred | Higher order methods: Estimate sequence methods (Baes 2009) http://www.optimization-online.org/DB_FILE/2009/08/2372.pdf | Slides |
| Nov 25 | Wu | Higher order methods: Bi-level unconstrained minimization (Nesterov 2020) https://dial.uclouvain.be/pr/boreal/object/boreal:227219 | Slides |
| Dec 2 | Simar | Other appl: Tensors in deep neural networks (Novikov et al. 2015) https://papers.nips.cc/paper/5787-tensorizing-neural-networks | Slides |
| Dec 9 | Adam | Other appl: Tensor completion in visual data (Liu et al. 2013) https://www.cs.rochester.edu/u/jliu/paper/Ji-ICCV09.pdf | Slides |
Summer term 2020 – Optimal Control
Every Wednesday at 1:00 PM Online
| Date | Presenter | Topic |
| Jul 8 | Wilder | Introduction and Overview – [pdf slides] |
| Jul 15 | Cathy | Optimal Control and Dynamical Systems – [pdf slides] |
| Jul 22 | Ben | Classic Control and Dynamic Programming – [pdf slides] |
| Jul 29 | Betty | Iterative LQR and Guided Policy Search – [pdf slides] |
| Aug 5 | Joey | Sample Complexity of the Linear Quadratic Regulator – [pdf slides] |
| Aug 12 | Fred | Model predictive control and Safe RL – [pdf slides] |
| Aug 19 | Aaron | End-to-End Training of Deep Visuomotor Policies – N/A |
| Aug 26 | Dylan | Combining Optimal control and Learning for Visual Navigation in Novel Environments – [pdf slides] |
Winter term 2 2020 – Causality
Every Wednesday in room ICICS X836 at 1:00 PM
| Date | Presenter | Topic |
| Jan 29 | Jason | Motivation – [pdf slides] |
| Feb 5 | Cathy | Classical approaches – [pdf slides] |
| Feb 12 | Ben | Counterfactual Inference with Deep Learning – [pdf slides] |
| Feb 26 | Aaron | Instrumental Variables – [pdf slides] |
| Mar 4 | Wu | Causal Inference with VAEs – [pdf slides] |
| Mar 11 | Betty | Causal Discovery – [pdf slides] |
| Mar 18 | Fred | Out-of-distribution Generalization |
| Mar 25 | Joey | Confounded Bandits |
| Apr 1 | Adam | Causality in Computer Vision |
| Apr 8 | Wilder | Causality in Reinforcement Learning |
| Apr 15 | Devon | Counterfactuals |
| Apr 22 | Alireza | Meta-learning Causal Structures |
Winter term 1 2019 – Why Deep Learning Works
Every 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 Learning
Every 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 Learning
Every 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 2
Every 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 Learning
Every 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 Models
Every 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 learning
Every 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 Learning
Every 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 Learning
Every 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 – Miscellaneous
Every 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 methods
Every 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 optimization
Every 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 models
Room 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] |