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Regret Minimization for Online Buffering Problems Using the Weighted Majority Algorithm.
6 references·2 citing·33 second hop
Regret Minimization for Online Buffering Problems Using the Weighted Majority Algorithm. (2010)Regret Minimization for Online Buffering …Learning, Regret Minimization, and Equilibria (2007)Learning, Regret Minimization…Online convex optimization in the bandit setting: gradient descent without a gradient (2004)Online convex optimization in…Efficient algorithms for online decision problems (2004)Efficient algorithms for onli…Online convex programming and generalized infinitesimal gradient ascent (2003)Online convex programming and…Regret in the On-Line Decision Problem (1999)Regret in the On-Line Decisio…How to use expert advice (1993)How to use expert adviceExploiting easy data in online optimization (2014)Exploiting easy data in onlin…Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions (2013)Online Learning in Markov Dec…Exponentiated Gradient versus Gradient Descent for Linear Predictors (1997)Nearly Tight Bounds for the Continuum-Armed Bandit Problem (2004)Multi-armed bandits in metric spaces (2008)Linearly Parameterized Bandits (2010)Introduction to Stochastic Programming (2011)The Asymptotic Theory of Extreme Order Statistics (1990)Derandomizing stochastic prediction strategies (1997)Minimizing Regret: The General Case (1999)Regret bounds for prediction problems (1999)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)The Theory of Learning in Games (1998)Prediction, Learning, and Games (2006)Multiplicative updates outperform generic no-regret learning in congestion games (2009)Fast Convergence of Regularized Learning in Games (2015)Intrinsic Robustness of the Price of Anarchy (2015)Contributions to the theory of games. (1960)A General Class of Adaptive Strategies (2001)Stochastic Approximations and Differential Inclusions (2005)Learning by trial and error (2008)Games and Decisions (2015)Finite-time Analysis of the Multiarmed Bandit Problem (2002)Using confidence bounds for exploitation-exploration trade-offs (2003)Improved Algorithms for Linear Stochastic Bandits (2011)Logarithmic Regret for Adversarial Online Control (2020)Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism (2020)Nonstationary Reinforcement Learning: The Blessing of (More) Optimism (2023)How to use expert advice (1997)Algorithms for portfolio management based on the Newton method (2006)Combinatorial bandits (2012)From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)Online Learning and Online Convex Optimization (2011)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Ad click prediction (2013)
this work references cited by second hop

References

  • 2003Online convex programming and generalized infinitesimal gradient ascent1,733 cited
  • 2004Online convex optimization in the bandit setting: gradient descent without a gradient599 cited
  • 2004Efficient algorithms for online decision problems595 cited
  • 1999Regret in the On-Line Decision Problem302 cited
  • 2007Learning, Regret Minimization, and Equilibria183 cited
  • 1993How to use expert advice129 cited

Cited by

  • 2013Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions44 cited
  • 2014Exploiting easy data in online optimization37 cited

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