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Creators/Authors contains: "Takac, Martin"

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  1. ; ; ; ; (, AISTATS)
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Journal of machine learning research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; ; ; (, Journal of machine learning research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; (, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)
    Low-rank methods for semi-definite programming (SDP) have gained a lot of interest recently, especially in machine learning applications. Their analysis often involves determinant-based or Schatten-norm penalties, which are difficult to implement in practice due to high computational efforts. In this paper, we propose Entropy-Penalized Semi-Definite Programming (EP-SDP), which provides a unified framework for a broad class of penalty functions used in practice to promote a low-rank solution. We show that EP-SDP problems admit an efficient numerical algorithm, having (almost) linear time complexity of the gradient computation; this makes it useful for many machine learning and optimization problems. We illustrate the practical efficiency of our approach on several combinatorial optimization and machine learning problems. 
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    Full Text Available 
  5. ; ; ; ; ; ; (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ; ; (, IEEE Transactions on Smart Grid)
    Full Text Available