20 citations · 76 across the 13 of their papers we have counts for
15 papers
Variance Reduction for Inverse Trace Estimation via Random Spanning Forests
Yusuf Yigit Pilavci, Pierre-Olivier Amblard, Simon Barthelme +1
The trace $\tr(q(\ma{L} + q\ma{I})^{-1})$, where $\ma{L}$ is a symmetric diagonally dominant matrix, is the quantity of interest in some machine learning problems. However, its dir…
Graph Tikhonov Regularization and Interpolation via Random Spanning Forests
Yusuf Pilavci, Pierre-Olivier Amblard, Simon Barthelme +1
Novel Monte Carlo estimators are proposed to solve both the Tikhonov regularization (TR) and the interpolation problems on graphs. These estimators are based on random spanning for…
Spectral properties of kernel matrices in the flat limit
Simon Barthelmé, Konstantin Usevich
Kernel matrices are of central importance to many applied fields. In this manuscript, we focus on spectral properties of kernel matrices in the so-called ``flat limit'', which occu…
Smoothing graph signals via random spanning forests
Yusuf Y. Pilavci, Pierre-Olivier Amblard, Simon Barthelmé +1
Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo…
Determinantal Point Processes for Coresets
Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard
When faced with a data set too large to be processed all at once, an obvious solution is to retain only part of it. In practice this takes a wide variety of different forms, and am…
Optimized Algorithms to Sample Determinantal Point Processes
Nicolas Tremblay, Simon Barthelme, Pierre-Olivier Amblard
In this technical report, we discuss several sampling algorithms for Determinantal Point Processes (DPP). DPPs have recently gained a broad interest in the machine learning and sta…
Graph sampling with determinantal processes
Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, ie, in cases where the spectr…
Bounding errors of Expectation-Propagation
Guillaume P Dehaene, Simon Barthelmé
Expectation Propagation is a very popular algorithm for variational inference, but comes with few theoretical guarantees. In this article, we prove that the approximation errors ma…
The Poisson transform for unnormalised statistical models
Simon Barthelmé, Nicolas Chopin
Contrary to standard statistical models, unnormalised statistical models only specify the likelihood function up to a constant. While such models are natural and popular, the lack…
Spatial statistics and attentional dynamics in scene viewing
Ralf Engbert, Hans A. Trukenbrod, Simon Barthelmé +1
In humans and in foveated animals visual acuity is highly concentrated at the center of gaze, so that choosing where to look next is an important example of online, rapid decision…
Fast matrix computations for functional additive models
Simon Barthelme
It is common in functional data analysis to look at a set of related functions: a set of learning curves, a set of brain signals, a set of spatial maps, etc. One way to express rel…
Visualizing the Effects of a Changing Distance on Data Using Continuous Embeddings
Gina Gruenhage, Manfred Opper, Simon Barthelme
Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints. For complex datasets it is hard to…
Modelling fixation locations using spatial point processes
Simon Barthelmé, Hans Trukenbrod, Ralf Engbert +1
Whenever eye movements are measured, a central part of the analysis has to do with where subjects fixate, and why they fixated where they fixated. To a first approximation, a set o…
Some discussions of D. Fearnhead and D. Prangle's Read Paper "Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation"
Christophe Andrieu, Simon Barthelme, Nicolas Chopin +11
This report is a collection of comments on the Read Paper of Fearnhead and Prangle (2011), to appear in the Journal of the Royal Statistical Society Series B, along with a reply fr…
Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods"
Simon Barthelme, Magali Beffy, Nicolas Chopin +5
This is a collection of discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead, to appear in the Journal of the Royal Statistical…