21 citations · 58 across the 10 of their papers we have counts for
10 papers
Storage capacity of perceptron with variable selection
Yingying Xu, Masayuki Ohzeki, Yoshiyuki Kabashima
A central challenge in machine learning is to distinguish genuine structure from chance correlations in high-dimensional data. In this work, we address this issue for the perceptro…
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood
Kimmo Suotsalo, Yingying Xu, Jukka Corander +1
Learning vector autoregressive models from multivariate time series is conventionally approached through least squares or maximum likelihood estimation. These methods typically ass…
Learning performance in inverse Ising problems with sparse teacher couplings
Alia Abbara, Yoshiyuki Kabashima, Tomoyuki Obuchi +1
We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teache…
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study
Johan Pensar, Yingying Xu, Santeri Puranen +3
Learning the undirected graph structure of a Markov network from data is a problem that has received a lot of attention during the last few decades. As a result of the general appl…
Approximate message passing for nonconvex sparse regularization with stability and asymptotic analysis
Ayaka Sakata, Yingying Xu
We analyse a linear regression problem with nonconvex regularization called smoothly clipped absolute deviation (SCAD) under an overcomplete Gaussian basis for Gaussian random data…
Statistical properties of interaction parameter estimates in direct coupling analysis
Yingying Xu, Erik Aurell, Jukka Corander +1
We consider the statistical properties of interaction parameter estimates obtained by the direct coupling analysis (DCA) approach to learning interactions from large data sets. Ass…
Statistical mechanics analysis of thresholding 1-bit compressed sensing
Yingying Xu, Yoshiyuki Kabashima
The one-bit compressed sensing framework aims to reconstruct a sparse signal by only using the sign information of its linear measurements. To compensate for the loss of scale info…
Statistical inference with probabilistic graphical models
Angélique Drémeau, Christophe Schülke, Yingying Xu +1
These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les…
Bayesian signal reconstruction for 1-bit compressed sensing
Yingying Xu, Yoshiyuki Kabashima, Lenka Zdeborova
The 1-bit compressed sensing framework enables the recovery of a sparse vector x from the sign information of each entry of its linear transformation. Discarding the amplitude info…
Statistical mechanics approach to 1-bit compressed sensing
Yingying Xu, Yoshiyuki Kabashima
Compressed sensing is a technique for recovering a high-dimensional signal from lower-dimensional data, whose components represent partial information about the signal, utilizing p…