Open Problem: Improper Learning of Mixtures of Gaussians

Elad Hazan, Roi Livni

Research output: Contribution to journalConference articlepeer-review


We ask whether there exists an efficient unsupervised learning algorithm for mixture of Gaussians in the over-complete case (number of mixtures is larger than the dimension). The notion of learning is taken to be worst-case compression-based, to allow for improper learning.

Original languageEnglish (US)
Pages (from-to)3399-3402
Number of pages4
JournalProceedings of Machine Learning Research
StatePublished - 2018
Event31st Annual Conference on Learning Theory, COLT 2018 - Stockholm, Sweden
Duration: Jul 6 2018Jul 9 2018

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


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