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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You can receive announcements about the reading group by joining our mailing list. To join the mailing list, please use an academic email address and send an email to mlrg-l-request@cs.ubc.ca with the word “join” (no quotes) in the body of the message. This will subscribe the sender’s email address to the mailing list. To unsubscribe please follow the same instructions but replace “join” with “leave” (no quotes) in the message body.
Upcoming Talks
Summer 2024 – Choose Your Own Topic
Every Monday at 1:00PM in x836.
| Date | Presenter | Topic/ Paper | Slides |
|---|---|---|---|
| Jun. 17 | Betty | Greedy Layerwise Learning Can Scale to ImageNet https://proceedings.mlr.press/v97/belilovsky19a/belilovsky19a.pdf | Slides |
| Jun. 24 | Mark | Understanding and improving the numerical optimization underlying modern machine learning | Slides |
| Jul. 1 | none | Canada Day | . |
| Jul. 8 | Alan | Mamba: Linear-Time Sequence Modeling with Selective State Spaces https://arxiv.org/pdf/2312.00752 | Slides |
| Jul.15 | Issam | InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks https://arxiv.org/abs/2312.14238 | Slides |
| Jul. 22 | none | ICML / ISMP | |
| Jul. 29 | Joshua | The Platonic Representation Hypothesis https://arxiv.org/pdf/2405.07987 | Slides |
| Aug. 5 | none | BC Day | |
| Aug. 12 | Curtis | On the Interplay Between Stepsize Tuning and Progressive Sharpening https://opt-ml.org/papers/2023/paper91.pdf Stepping on the Edge: Curvature Aware Learning Rate Tuners https://arxiv.org/pdf/2407.06183 |
Past Sessions
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.
| 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 | Slides | 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] |