Optimization for machine learning

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Other Authors: Sra, Suvrit, 1976-, Nowozin, Sebastian, 1980-, Wright, Stephen J., 1960-
Format: EBOOK
Published: Cambridge, Mass. : MIT Press, c2012.
Series:Neural information processing series.
Online Access:Go to eBook
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Table of Contents:
  • Introduction : Optimization and machine learning / S. Sra, S. Nowozin, and S.J. Wright
  • Convex optimization with sparsity-inducing norms / F. Bach, R. Jenatton, J. Mairal, and G. Obozinski
  • Interior-point methods for large-scale cone programming / M. Andersen, J. Dahl, Z. Liu, and L. Vanderberghe
  • Incremental gradient, subgradient, and proximal methods for convex optimization : a survey / D. P. Bertsekas
  • First-order methods for nonsmooth convex large-scale optimization, I : general purpose methods / A. Juditsky and A. Nemirovski
  • First-order methods for nonsmooth convex large-scale optimization, II : utilizing problem's structure / A. Juditsky and A. Nemirovski
  • Cutting-plane methods in machine learning / V. Franc, S. Sonnenburg, and T. Werner
  • Introduction to dual decomposition for inference / D. Sontag, A. Globerson, and T. Jaakkola
  • Augmented Lagrangian methods for learning, selecting, and combining features / R. Tomioka, T. Suzuki, and M. Sugiyama
  • The convex optimization approach to regret minimization / E. Hazan
  • Projected Newton-type methods in machine learning / M. Schmidt, D. Kim, and S. Sra
  • Interior-point methods in machine learning / J. Gondzio
  • The tradeoffs of large-scale learning / L. Bottou and O. Bousquet
  • Robust optimization in machine learning / C. Caramanis, S. Mannor, and H. Xu
  • Improving first and second-order methods by modeling uncertainty / N. Le Roux, Y. Bengio, and A. Fitzgibbon
  • Bandit view on noisy optimization / J.-Y. Audibert, S. Bubeck, and R. Munos
  • Optimization methods for sparse inverse covariance selection / K. Scheinberg and S. Ma
  • A pathwise algorithm for covariance selection / V. Krishnamurthy, S. D. Ahipasaoglu, and A. d'Aspremont.