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Time & Location
Mondays 1:30pm-3pm, Gatsby 3th floor seminar room at Sainsbury Wellcome Centre 46 Cleveland Street. London. W1T 4JG.
Calendar
| Date | Presenter | Topic | Reading | Supplement |
|---|---|---|---|---|
| 26 Sep 2016 | Joana | Bayesian model selection and information criteria | ||
| 19 Sep 2016 | Aapo | Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA | Hyvarinen & Morioka, 2016 | |
| 5 Sep 2016 | Wittawat | Gaussian Process Random Fields | Moore & Russell, 2015 | |
| 25 July 2016 | Arthur | Training Input-Output Recurrent Neural Networks through Spectral Methods | Sedghi & Anandkumar, 2016 | |
| 18 July 2016 | Gergo | Geometry of nonlinear least squares with applications to sloppy models and optimization | Transtrum et al., 2011 | |
| 11 July 2016 | Vincent | A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation | Bui et al., 2016 | |
| 27 June 2016 | Zoltan | General notions of statistical depth function | Zuo & Serfling, 2000 | slides |
| 20 June 2016 | Heiko | Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics | Gutmann & Hyvarinen, 2012 | |
| 13 June 2016 | Alex | A Mathematical Motivation for Complex-Valued Convolutional Networks | Tygert et. al., 2016 | |
| 6 June 2016 | Fredrik | Understanding predictive information criteria for Bayesian models, Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models | Gelman et. al., 2013, Vehtari et. al., 2016 | |
| 23 May 2016 | Wittawat | Bayesian Learning of Kernel Embeddings | Flaxman et. al., 2016 | |
| 9 May 2016 | Carlos | Estimation theory for stochastic gradient descent | - | |
| 25 Apr 2016 | Kevin Li | A Probabilistic Theory of Deep Learning | Patel et. al., 2015 | |
| 18 Apr 2016 | Vincent | Generalized Additive Models: An Introduction with R | Wood 2006 | |
| 11 Apr 2016 | Maneesh | On Autoencoders and Score Matching for Energy Based Models | Swersky et. al., 2011 | |
| 4 Apr 2016 | Zoltan | Nonparametric Independence Testing for Small Sample Sizes | Ramdas & Wehbe, 2015 | slides |
| 21 Mar 2016 | Heiko | Learning Structured Densities via Infinite Dimensional Exponential Families | Sun et al., 2015 | |
| 29 Feb 2016 | Wittawat | Bayesian Indirect Inference Using a Parametric Auxiliary Model | Drovandi et al., 2015 | slides |
| 15 Feb 2016 | Song Liu | Estimating Density Ratio: Learning Changes of Patterns | Song Liu's homepage | slides |
| 8 Feb 2016 | Vincent | MCMC for Variationally Sparse Gaussian Processes | Hensman et al., 2015 | |
| 11 Jan 2016 | Zoltan | Automatic differentiation | Baydin et al., 2015, Hoffmann 2015 | slides |
| 30 Nov 2015 | Vincent | On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes | Matthews et al., 2015 | |
| 23 Nov 2015 | Wittawat | On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives | Ramdas et al., 2014 | |
| 16 Nov 2015 | Heiko | NYTRO: When Subsampling Meets Early Stopping | Angles et al., 2015 | |
| 9 Nov 2015 | Arthur | What Regularized Auto-Encoders Learn from the Data Generating Distribution | Alain & Bengio, 2012 | |
| 2 Nov 2015 | Zoltan | Kernels Based Tests with Non-asymptotic Bootstrap Approaches for Two-sample Problems | Fromont et al., 2012 | slides |
| 26 Oct 2015 | Mijung | Robust and Private Bayesian Inference | Dimitrakakis et al., 2014 | |
| | Maneesh | Bayesian measures of model complexity and fit | Spiegelhalter et al., 2002 | |
| 12 Oct 2015 | Wittawat | Estimating Mutual Information by Local Gaussian Approximation | Gao et al., 2015 | |
| 5 Oct 2015 | Kacper | Optimal Detection of Sparse Principal Components in High Dimension | Berthet and Rigollet, 2015 | |
| 4 Aug 2015 | Wittawat | Landmarking Manifolds with Gaussian Processes | Liang and Paisley, 2015 | slides |
| 20 July 2015 | Tom | Safe Exploration for Optimization with Gaussian Processes | Sui et al., 2015 | |
| 29 June 2015 (1-19 Torrington Place Room 102 Statistics Lecture Room from 1-3pm) | Anna Choromanska | The Loss Surfaces of Multilayer Networks | homepage | |
| 17 June 2015 (Wed) at 14:30 | Arthur, Heiko | Stochastic Gradient Hamiltonian Monte Carlo | Chen et al., 2014 | |
| 8 June 2015 | Heiko | Hamiltonian ABC | Meeds et al., 2015 | slides |
| 18 May 2015 | Wittawat | Deep Exponential Families | Ranganath et al., 2015 | slides |
| 11 May 2015 | Maneesh | Section 7: Convex Relaxations and Upper Bounds | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | |
| 27 April 2015 | Kacper | Section 6: Variational Methods in Parameter Estimation | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | |
| 20 April 2015 | Wittawat | Section 5: Mean field methods | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | slides |
| 23 Mar 2015 | Vincent | Section 4.3: Expectation propagation | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | slides |
| 12 Mar 2015 (16:00) | Zoltan | Proof of random Fourier features | Random features (Rahimi & Recht) | slides |
| 2 Mar 2015 | - | No meeting. UAI deadline. Cosyne. | ||
| 23 Feb 2015 | Vincent, Alessandro | Section 4.1, 4.2: Bethe-Kikuchi | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | slides |
| 16 Feb 2015 | Wittawat, Heiko | Chapter 3, 4.1: Sum-Product, Bethe | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | slides |
| 9 Feb 2015 | Maneesh | Chapter 3: Graphical Models as Exponential Families | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | Maneesh's lecture slides |
| 26 Jan 2015 | Tom, Maneesh | Chapter 1-3: Background on variational inference | Graphical Models, ExpFam, Variational Inference by Wainwright & Jordan, 2008 | Tom's slides |
| 20 Jan 2015 | All | General meeting | . | |
| 24 Nov 2014 | Arthur, Zoltan, Tom | NIPS preview | Arthur on Ba & Caruana, NIPS 2014, Zoltan on Yang et. al., ICML 2014, Tom on Frigola et. al., NIPS 2014 | Slides by Zoltan |
| 17 Nov 2014 | Mijung | Auto-Encoding Variational Bayes | Kingma & Welling, 2013 | |
| 3 Nov 2014 | - | No meeting | CSML Master class by Sham Kakade. event page. | |
| 27 Oct 2014 | Tom | A Spectral Algorithm for Learning Hidden Markov Models. | Hsu et. al., 2009 | |
| 20 Oct 2014 | Zoltan | Scalable Kernel Methods via Doubly Stochastic Gradients | Dai et. al., 2014 | Zoltan's slides |
| 13 Oct 2014 | Maneesh | spectral methods for latent time series models, SSID, spectral HMM | ML course slides | |
| 6 Oct 2014 | Balaji | The Consensus Monte Carlo Algorithm | Scott et. al., 2013 | |
| 29 Sep 2014 | MLJC members | Meeting | ||
| 21 Aug 2014 | Vincent | Gaussian Processes for Underdetermined Source Separation | Liutkus, A. et. al., 2011 | |
| 14 Aug 2014 | Dino, Balaji, Heiko | Firefly Monte Carlo: Exact MCMC with Subsets of Data | Maclaurin & Adams, 2014 | |
| 7 Aug 2014 | Laurence | Bayesian Learning via Stochastic Gradient Langevin Dynamics | Welling & Teh, 2011 (ICML) | |
| (2pm) 31 July 2014 | Mijung | Distributed Stochastic Gradient MCMC | Ahn et al., 2014 (ICML) | |
| 23,30 May, 6 June 2014 | - | No meeting | (preparation for NIPS) | |
| 16 May 2014 | Zoltan | Fastfood (Fast kernel approximation methods) | Fastfood (Le et al., 2013) | Slides by Zoltan |
| 9 May 2014 | Dino, Arthur | Random features & Random kitchen sinks | Random features (Rahimi & Recht). Random kitchen sinks (Rahimi & Recht) | Random features by Arthur, Random kitchen sinks by Dino |
| 18 Apr 2014 | - | No meeting. UCL Easter closure. | ||
| 11 Apr 2014 | - | No meeting. | ||
| 4 Apr 2014 | Balaji | Variational Learning of Inducing Variables in Sparse Gaussian Processes | Titsias 2009 (AISTATS) | slides |
| 28 Mar 2014 | Heiko | Bayesian Gaussian Process Latent Variable Model | Titsias & Lawrence 2010 (AISTATS) | slides |
start.txt · Last modified: 2016/10/14 13:45 by admin
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