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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
19 Oct 2015 (cancelled) 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
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