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New Adaptive Algorithms for Online Classification
4 references·0 citing·19 second hopsampled
New Adaptive Algorithms for Online Classification (2010)New Adaptive Algorithms for Online Classi…Extracting certainty from uncertainty: regret bounded by variation in costs (2010)Extracting certainty from unc…Online Learning: Theory, Algorithms, and Applications (2007)Online Learning: Theory, Algo…A primal-dual perspective of online learning algorithms (2007)A primal-dual perspective of …Prediction, Learning, and Games (2006)Prediction, Learning, and Gam…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Online Learning and Online Convex Optimization (2011)Online Learning and Online Convex Optimization (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Theory of games and economic behavior (2019)The Nonstochastic Multiarmed Bandit Problem (2002)A parameter-free hedging algorithm (2009)A new understanding of prediction markets via no-regret learning (2010)Online Learning: A Modern Introduction Using Convex Optimization (2019)Contributions to the Theory of Games. (1959)Exponentiated Gradient versus Gradient Descent for Linear Predictors (1997)Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions (2001)Efficient bandit algorithms for online multiclass prediction (2008)Interior-Point Methods for Full-Information and Bandit Online Learning (2012)Online convex programming and generalized infinitesimal gradient ascent (2003)On the Universality of Online Mirror Descent (2011)Regret in Online Combinatorial Optimization (2013)Learning in games with continuous action sets and unknown payoff functions (2018)
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References

  • 2006Prediction, Learning, and Games3,176 cited
  • 2007Online Learning: Theory, Algorithms, and Applications297 cited
  • 2007A primal-dual perspective of online learning algorithms169 cited
  • 2010Extracting certainty from uncertainty: regret bounded by variation in costs113 cited

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