Welcome to the website for the NIPS 2012 workshop on spectral algorithms for latent variable models! The workshop will be held on December 7th, 2012 in Lake Tahoe, Nevada, USA. We are currently soliciting works for oral and poster presentations during the workshop. See the Call for Papers and other tabs for more details.


Recently, linear algebra techniques have given a fundamentally different perspective for learning and inference in latent variable models. Exploiting the underlying spectral properties of the model parameters has led to fast, provably consistent methods for structure and parameter learning that stand in contrast to previous approaches, such as Expectation Maximization, which suffer from local optima and slow convergence. Furthermore, these techniques have given insight into the nature of latent variable models.

In this workshop, via a mix of contributed/invited talks, posters, and discussion, we seek to explore the theoretical and applied aspects of spectral methods including the following major themes:

  • How can spectral techniques help us develop fast and local minima free solutions to real world problems involving latent variables in natural language processing, dynamical systems, computer vision etc. where existing methods such as Expectation Maximization are unsatisfactory?
  • How can these approaches lead to a deeper understanding and interpretation of the complexity of latent variable models?