FANDOM


This page contains resources about Online Learning and Sequential Prediction.

Subfields and Concepts Edit

  • Recursive Least Squares
  • Mini-Batch Learning
    • Mini-Batch Gradient Descent Methods
  • Decision Theory
  • Information Theory
    • Entropy
    • Kullback-Leibler (KL) Divergence
  • Game Theory
    • Minimax Theorem
    • Blackwell's Approachability
  • Online Dictionary Learning
  • Online Algorithms
    • Wake-Sleep Algorithm
    • Auto-Encoding Variational Bayes (AEVB) Algorithm
  • Online Convex Optimization
    • Regret Bound
    • Bregman Divergence
    • No-regret Learning
    • Online Gradient Descent
    • Online Subgradient Descent
    • Mirror Descent
    • Stochastic Gradient Descent (SGD)
    • Mini-batch Gradient Descent Methods
    • Follow The Regularized Leader (FTRL)
    • Multi-Armed Bandit (MAB)
    • Regularization
      • L2-regularization / Tikhonov regularization / Ridge regression
      • L1-regularization / Least absolute shrinkage and selection operator (LASSO)
      • Matrix Regularization

Online Courses Edit

Video Lectures Edit


Lecture Notes Edit

Books and Book Chapters Edit

  • Hazan, E. (2015). Introduction to online convex optimization. Foundations and Trends® in Optimization2(3-4), 157-325.
  • Theodoridis, S. (2015). "Chapter 8: Parameter Learning: A Convex Analytic Path". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
  • Sra, S., Nowozin, S., & Wright, S. J. (2012). Optimization for machine learning. MIT Press.
  • Hazan, E. (2011). "Chapter 10: The Convex Optimization Approach to Regret Minimization". Optimization for machine learning. MIT Press.
  • Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends® in Machine Learning4(2), 107-194.

Scholarly Articles Edit

  • Villa, S., Rosasco, L. & Poggio, T. (2013). On Learning, Complexity and Stability. arXiv preprint arXiv:1303.5976.
  • Arora, S., Hazan, E., & Kale, S. (2012). The Multiplicative Weights Update Method: A Meta-Algorithm and Applications. Theory of Computing8(1), 121-164.
  • Shalev-Shwartz, S. (2011). Online learning and online convex optimization. Foundations and Trends® in Machine Learning4(2), 107-194.
  • Abernethy, J., Bartlett, P. L., & Hazan, E. (2011). Blackwell Approachability and No-Regret Learning are Equivalent. In COLT (pp. 27-46).
  • Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2009). Online dictionary learning for sparse coding. In Proceedings of the 26th annual international conference on machine learning (pp. 689-696). ACM.
  • Ying, Y., & Pontil, M. (2008). Online gradient descent learning algorithms. Foundations of Computational Mathematics8(5), 561-596.
  • Shalev-Shwartz, S. (2007). Online Learning: Theory, Algorithms, and Applications. PhD Dissertation, Hebrew University.
  • Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press.
  • Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th International Conference on Machine Learning (pp. 928–936).
  • Dietterich, T. G. (2002). Machine learning for sequential data: A review. In Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) (pp. 15-30). Springer Berlin Heidelberg.

See also Edit

Other resources Edit