Meetings Archive

MT 2017

Date	Presenter	Topic	Materials
02/11/2017	Leon	(Almost) No Label No Cry (NIPS 2014)	paper
16/11/2017	Michael	Kernel Feature Selection via Conditional Covariance Minimization (NIPS 2017)	paper
23/11/2017	Dino	On kernel methods for covariates that are rankings	paper

TT 2017

Date	Presenter	Topic	Materials
23/06/2017	Hyunjik	Asynchronous Distributed Variational Gaussian Process for Regression (ICML 2017)	paper
	Jean-Francois	Non-Stationary Spectral Kernels	paper
21/07/2017	Kurt Cutajar (EURECOM)	Bayesian Inference of Log Determinants (UAI 2017)	paper
		Random Feature Expansions for Deep Gaussian Processes (ICML 2017)	paper
18/08/2017	Dino	Nyström Method with Kernel K-means++ Samples as Landmarks	paper
15/09/2017	Leon	Estimating Labels from Label Proportions	paper
22/09/2017	Zhu	Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees	paper
29/09/2017	Tim	Variational Fourier Features for Gaussian Processes	paper

HT 2017

Date	Presenter	Topic	Materials
19/01/2017	Leon	End-to-End Kernel Learning with Supervised Convolutional Kernel Networks	Paper
26/01/2017	Qinyi	Joint Quantile Regression in Vector-Valued RKHSs	Paper
02/02/2017	Seth	Kernel Mean Embedding of Distributions: A Review and Beyond	Paper
09/02/2017	Thibaut	Step Size Adaptation in Reproducing Kernel Hilbert Space	Paper
16/02/2017	Tamara	The Randomized Causation Coefficient	Paper
23/02/2017	Dino	The Kendall and Mallows Kernels for Permutations	Paper
02/03/2017	Hyunjik	Statistical Model Criticism using Kernel Two Sample Tests	Paper
09/03/2017		No Meeting
16/03/2017	Jovana	Learning with Hierarchical Gaussian Kernels	Paper

MT 2016

Date	Presenter	Topic	Materials
15/09/2016		Organisational Meeting
22/09/2016		Ch. 1 of Wahba 1990	book
29/09/2016		No Meeting
06/10/2016		see Arthur Gretton's talk at the Workshop on Stein's method (no separate meeting)	Paper
13/10/2016	Tamara	Bayesian Nonparametric Kernel Learning	Paper
20/10/2016	Hyunjik	Variational Learning of Inducing Variables in Sparse Gaussian Processes	Paper
27/10/2016	Seth	Scalable spatiotemporal point process modeling with Gaussian Processes	Paper
03/11/2016		No Meeting
10/11/2016	Qinyi	Preconditioning Kernel Matrices	Paper
17/11/2016	Leon	Interpretable Distribution Features with Maximum Testing Power	Paper
24/11/2016	Jovana	Kernel Bayesian Inference with Posterior Regularization	Paper
01/12/2016		No Meeting

TT 2016

Date	Presenter	Topic	Materials
04/04/2016	Roman Garnett (Washington University in St. Louis)	Active Learning of Hyperparameters for Gaussian Processes
11/04/2016	Paul Rubenstein (University of Cambridge / MPI Tubingen)	A Kernel Test for Three-Variable Interactions with Random Processes	arXiv
25/04/2016, LG.03	Ingmar Schuster (Universite Paris-Dauphine)	Kernel Sequential Monte Carlo	arXiv
Tue, 03/05/2016, LG.03, 3pm	Bharath Sriperumbudur (Pennsylvania State University)	Minimax Estimation of Kernel Mean Embeddings	arXiv
23/05/2016, 3.30pm	Thibaut Lienart (internal)	Training generative NN via MMD opt	arXiv
23/05/2016, 4pm	Tamara Fernandez (internal)	RKHS of Gaussian priors	arXiv
Wed, 15/06/2016, 3.30pm, LG.04	Qinyi Zhang (internal)	A Kernel Test of Goodness of Fit	arXiv
27/06/2016	Xiaoyu Lu (internal)	Risk-Sensitive Model Predictive Control with Gaussian Process Models	paper

HT 2016

Date	Presenter	Topic	Materials
18/01/2016	Owen Thomas	Scalable Kernel Methods via Doubly Stochastic Gradients	http://arxiv.org/pdf/1407.5599.pdf
25/01/2016	Zhu Li	Sharp analysis of low-rank kernel matrix approximations	http://arxiv.org/abs/1208.2015
01/02/2016	F-X Briol	Probabilistic Integration	http://arxiv.org/abs/1512.00933
08/02/2016	Jon Cockayne	Bayesian Inverse Problems in PDEs with Probabilistic Models for Numerical Error	accompanying slides
15/02/2016	Arnold Salas	epsilon-Gaussian process regression	accompanying slides
22/02/2016	Jovana Mitrovic	Deep Kernel Learning	[paper] (http://arxiv.org/pdf/1511.02222v1.pdf)
29/02/2016	Qinyi Zhang	Nyström Computational Regularization	http://arxiv.org/abs/1507.04717
07/03/2016 3.30pm	Hyunjik Kim	Structure Discovery in Nonparametric Regression through Compositional Kernel Search	http://arxiv.org/pdf/1302.4922.pdf
07/03/2016 4.30pm	Hiroaki Imai	Density Estimation in Infinite Dimensional Exponential Families	paper

2015

Date	Presenter	Topic	Materials
Fri, 04/12/2015, 10am, OCGF seminar room	Krikamol Muandet	Towards a Learning Theory of Cause-Effect Inference	arxiv
Tue, 01/12/2015, 10am, OCGF seminar room	Krikamol Muandet	Kernel Mean Shrinkage Estimators (and friends)	arxiv
23/11/2015	Xiaoyu Lu	Feature Hashing for Large Scale Multitask Learning	pdf
16/11/2015	Dino Sejdinovic	Spherical Random Features for Polynomial Kernels	pdf
09/11/2015	no meeting
02/11/2015	Heiko Strathmann	Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families	paper, blogpost, Og1, Og2
26/10/2015	Seth Flaxman	Bayesian Kernel Models	main focus: paper 1 supplementary paper 2
19/10/2015 3.30pm	Tamara Fernandez-Aguilar	Posterior Consistency in Nonparametric Regression Problems under Gaussian Process Priors	paper
12/10/2015	Qinyi Zhang	Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations	paper
05/10/2015 3.30pm	Jovana Mitrovic	Two-stage Sampled Learning Theory on Distributions	[DistReg] (http://arxiv.org/abs/1402.1754)
28/09/2015	no meeting
21/09/2015 2pm	Martin Strazar	Learning the kernel matrix via predictive low-rank approximations
14/09/2015	Gianni Franchi	Invariant Scattering Convolution Networks	paper 1, paper 2
07/09/2015	Zhu Li	On the Error of Random Fourier Features	paper
31/08/2015	no meeting
24/08/2015	Dan Fess	Determinantal Point Processes	paper
17/08/2015	Zoltan Szabo	Sketching for least squares problems and kernel ridge regression	paper1, paper2
10/08/2015	no meeting
03/08/2015	Francois-Xavier Briol	On the Equivalence between Quadrature Rules and Random Features	Bach2015
27/07/2015	no meeting
20/07/2015	Dino Sejdinovic	Random Features + Fastfood	Rahimi&Recht, FastFood
13/07/2015	Thibaut Lienart	Kernel Embeddings of Conditional Distributions	SigProcMag paper
06/07/2015	Dino Sejdinovic	Basic RKHS theory	notes, slides 1, slides 2