---
res:
  bibo_abstract:
  - We explore the problem of mean estimation for a high-dimensional binary symmetric
    Gaussian mixture model, where the label (sign) follows a time-inhomogeneous Markov
    chain. We propose a spectral estimator based on a partition of a subset of the
    samples to blocks. We develop a computationally efficient algorithm to find the
    optimal blocks, and derive minimax lower bounds on the estimation loss of any
    estimator, which establish the effectiveness of our proposed estimator. The resulting
    minimax rate illuminates the interplay between the sample size, dimension, signal
    strength, and the memory on the loss.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Abd
      foaf_name: El Latif Kadry, Abd
      foaf_surname: El Latif Kadry
  - foaf_Person:
      foaf_givenName: Yihan
      foaf_name: Zhang, Yihan
      foaf_surname: Zhang
      foaf_workInfoHomepage: http://www.librecat.org/personId=2ce5da42-b2ea-11eb-bba5-9f264e9d002c
    orcid: 0000-0002-6465-6258
  - foaf_Person:
      foaf_givenName: Nir
      foaf_name: Weinberger, Nir
      foaf_surname: Weinberger
  bibo_doi: 10.1109/ISIT63088.2025.11195426
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2157-8095
  - http://id.crossref.org/issn/9798331543990
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: Mean estimation in high-dimensional binary timeinhomogeneous Markov Gaussian
    mixture models@
...