15 citations · 67 across the 29 of their papers we have counts for
10 papers · 1 filter
A new class of colored Gaussian graphical models with explicit normalizing constants
Adam Chojecki, Piotr Graczyk, Hideyuki Ishi +1
We study Bayesian model selection in colored Gaussian graphical models (CGGMs), which combine sparsity of conditional independencies with symmetry constraints encoded by vertex- an…
Identifying Network Hubs with the Partial Correlation Graphical LASSO
Małgorzata Bogdan, Adam Chojecki, Ivan Hejný +2
Graphical LASSO (GLASSO) is a widely used method for estimating sparse precision matrices and learning undirected graphical models in high-dimensional settings. Because GLASSO pena…
From Graphical Lasso to Atomic Norms: High-Dimensional Pattern Recovery
Piotr Graczyk, Bartosz Kołodziejek, Hideto Nakashima +1
We study high-dimensional precision-matrix estimation with polyhedral atomic-norm penalties on the off-diagonal entries. This framework covers the graphical lasso, -ty…
Analysis of Conditional Randomisation and Permutation schemes with application to conditional independence testing
Małgorzata Łazęcka, Bartosz Kołodziejek, Jan Mielniczuk
We study properties of two resampling scenarios: Conditional Randomisation and Conditional Permutation schemes, which are relevant for testing conditional independence of discrete…
Graphical Gaussian models associated to a homogeneous graph with permutation symmetries
Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek
We consider multivariate centered Gaussian models for the random vector , whose conditional structure is described by a homogeneous graph and which is invariant…
Pattern recovery by SLOPE
Małgorzata Bogdan, Xavier Dupuis, Piotr Graczyk +4
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be eq…
Pattern recovery and signal denoising by SLOPE when the design matrix is orthogonal
Tomasz Skalski, Piotr Graczyk, Bartosz Kołodziejek +1
Sorted Penalized Estimator (SLOPE) is a relatively new convex regularization method for fitting high-dimensional regression models. SLOPE allows to reduce the model dimens…
Model selection in the space of Gaussian models invariant by symmetry
Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek +1
We consider multivariate centered Gaussian models for the random variable , invariant under the action of a subgroup of the group of permutations on $\{1,\ldot…
Location and scale behaviour of the quantiles of a natural exponential family
Mauro Piccioni, Bartosz Kołodziejek, Gérard Letac
Let be a probability on the real line generating a natural exponential family . Fix in We show that the property that $P_t((-\infty,t)…
Wishart laws and variance function on homogeneous cones
Piotr Graczyk, Hideyuki Ishi, Bartosz Kołodziejek
We present a systematic study of Riesz measures and their natural exponential families of Wishart laws on a homogeneous cone. We compute explicitly the inverse of the mean map and…