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[01211] Generalized and non-Gaussian Tensor Decompositions

  • Session Date & Time :
    • 01211 (1/2) : 1C (Aug.21, 13:20-15:00)
    • 01211 (2/2) : 1D (Aug.21, 15:30-17:10)
  • Type : Proposal of Minisymposium
  • Abstract : Tensor decompositions are a foundational unsupervised machine learning method for data science, with applications across all of science and engineering. Traditionally, tensor decompositions seek low-rank tensors that best fit the data with respect to the least squares loss. However, other choices of loss function can be more appropriate for non-Gaussian data such as count data, binary data, and data with outliers. This minisymposium presents state-of-the-art advances in developing efficient algorithms and rigorous theory for tensor decompositions with respect to general losses.
  • Organizer(s) : David Hong
  • Classification : 15A69
  • Speakers Info :
    • David Hong (University of Pennsylvania)
    • Anru Zhang (Duke University)
    • ‪Lieven De Lathauwer (KU Leuven)
    • Wenqiang Pu (Shenzhen Research Institute of Big Data)
    • Eric Phipps (Sandia National Labs)
    • Edgar Solomonik (University of Illinois at Urbana-Champaign)
  • Talks in Minisymposium :