1 citations · 1 across the 3 of their papers we have counts for
11 papers
Multivariate Super-Resolution without Separation
Bakytzhan Kurmanbek, Elina Robeva
In this paper we study the high-dimensional super-resolution imaging problem. Here we are given an image of a number of point sources of light whose locations and intensities are u…
The Set of Orthogonal Tensor Trains
Pardis Semnani, Elina Robeva
In this paper we study the set of tensors that admit a special type of decomposition called an orthogonal tensor train decomposition. Finding equations defining varieties of low-ra…
Learning Linear Non-Gaussian Graphical Models with Multidirected Edges
Yiheng Liu, Elina Robeva, Huanqing Wang
In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data. We build on an algori…
Orthogonal Decomposition of Tensor Trains
Karim Halaseh, Tommi Muller, Elina Robeva
In this paper we study the problem of decomposing a given tensor into a tensor train such that the tensors at the vertices are orthogonally decomposable. When the tensor train has…
Optimal Rates for Estimation of Two-Dimensional Totally Positive Distributions
Jan-Christian Hütter, Cheng Mao, Philippe Rigollet +1
We study minimax estimation of two-dimensional totally positive distributions. Such distributions pertain to pairs of strongly positively dependent random variables and appear freq…
Multi-trek separation in Linear Structural Equation Models
Elina Robeva, Jean-Baptiste Seby
Building on the theory of causal discovery from observational data, we study interactions between multiple (sets of) random variables in a linear structural equation model with non…