CSE 291 Paper List

Topics in Online Learning

  1. From Internal to External Regret. A. Blum, Y. Mansour, and R. Meir, COLT 2005
  2. Potential-based algorithms in on-line prediction and game theory N. Cesa-Bianchi and G. Lugosi, Machine Learning, 2003.
  3. Tracking the Best Expert. M. Herbster and M. Warmuth, Machine Learning, 1998.
  4. Regret to the Best vs. Regret to the Average. E. Even-Dar, M. Kearns, Y. Mansour, and J. Wortman, COLT 2007.
  5. A Random Walk Approach to Regret Minimization. H. Narayanan and A. Rakhlin, NIPS 2010.
  6. Online Passive Aggressive Algorithms , K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, Y. Singer, JMLR 2006
  7. Confidence-Weighted Linear Classification. M. Dredze, K. Crammer and F. Pereira, ICML 2008.
  8. A Stochastic View of Optimal Regret through Minimax Duality J. Abernathy, A. Agarwal, P. Bartlett, S. Rakhlin, COLT 2009.
  9. On the Generalization Ability of Online Learning Algorithms N. Cesa-Bianchi, A. Conconi, C. Gentile, IEEE Transactions on Information Theory, 2004.
  10. Blackwell approachability and low regret learning are equivalent J. Abernathy, P. Bartlett, E. Hazan, Arxiv, 2010.
  11. The Exponentiated Gradient vs. Gradient Descent for Linear Predictors J. Kivinen, M. Warmuth, Information and Computation, 1996.
  12. Agnostic Online Learning S. Ben-David, D. Pal, S. Shalev-Shwartz, COLT 2009.

Stochastic Optimization

  1. Online convex programming and generalized infinitesimal gradient ascent. M. Zinkevich, ICML 2003.
  2. Adaptive subgradient algorithms for online learning and stochastic optimization. J. Duchi, E. Hazan and Y. Singer, COLT 2010.
  3. Extracting Certainty from Uncertainty: Regret Bounded by Variation in Costs. E. Hazan and S. Kale, COLT 2008.
  4. Adaptive online gradient descent. P. Bartlett, E. Hazan and A. Rakhlin, NIPS 2007.
  5. Efficient Algorithms for Online Decision Problems. A. Kalai and S. Vempala, JCSS 2003.
  6. A Primal-Dual Perspective on Online-Learning Algorithms S. Shalev-Shwartz and Y. Singer, Machine Learning, 2007.

Bandit Algorithms

  1. Finite time analysis of the Multi-armed Bandit Problem P. Auer, N. Cesa-Bianchi, P. Fischer, Machine Learning, 2002.
  2. Online Convex Optimization in the Bandit Setting: Gradient Descent Without a Gradient A. Flaxman, A. Kalai, B. McMahan, SODA 2005.
  3. Competing in the dark: An Efficient Algorithm for Bandit Linear Optimization J. Abernathy, E. Hazan and S. Rakhlin, COLT 2008.
  4. Efficient bandit algorithms for online multiclass prediction. S. Kakade, S. Shalev-Shwartz, and A. Tewari, ICML 2008.
  5. The epoch-greedy algorithm for multiarmed bandits with side information, J. Langford and T. Zhang, NIPS 2007.
  6. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design , N. Srinivas, A. Krause, S. Kakade and M. Seeger, ICML 2010.