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Algorithm Selection as a Bandit Problem with Unbounded Losses
9 references·0 citing·27 second hop
Algorithm Selection as a Bandit Problem with Unbounded Losses (2010)Algorithm Selection as a Bandit Problem w…Data fusion (2009)Data fusionLearning dynamic algorithm portfolios (2007)Learning dynamic algorithm po…Improved second-order bounds for prediction with expert advice (2006)Improved second-order bounds …Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring (2006)Hannan Consistency in On-Line…An asymptotically optimal algorithm for the max k-armed bandit problem (2006)An asymptotically optimal alg…The max K-armed bandit: a new model of exploration applied to search heuristic selection (2005)The max K-armed bandit: a new…The Nonstochastic Multiarmed Bandit Problem (2002)The Nonstochastic Multiarmed …Gambling in a rigged casino: The adversarial multi-armed bandit problem (2002)Gambling in a rigged casino: …Some aspects of the sequential design of experiments (1952)Some aspects of the sequentia…An Introduction to Statistical Modeling of Extreme Values (2001)Finite-time Analysis of the Multiarmed Bandit Problem (2002)ε-first policies for budget-limited multi-armed bandits (2010)Bandit Problems, Sequential Allocation of Experiments. (1987)Bandits Games and Clustering Foundations (2010)Efficient Multi-Start Strategies for Local Search Algorithms (2011)Contributions to the Theory of Games (1952)A Simple Adaptive Procedure Leading to Correlated Equilibrium (2000)Prediction, Learning, and Games (2006)Multi-Armed Bandits: Theory and Applications to Online Learning in Networks (2019)A Stochastic Approximation Method (1951)Bandit Processes and Dynamic Allocation Indices (1979)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Extracting certainty from uncertainty: regret bounded by variation in costs (2010)Online Optimization with Gradual Variations (2012)The best of both worlds: stochastic and adversarial bandits (2012)Multi-Armed Bandit Allocation Indices. (1990)Adaptive Game Playing Using Multiplicative Weights (1999)Online convex optimization in the bandit setting: gradient descent without a gradient (2004)Multi-armed Bandit Algorithms and Empirical Evaluation (2005)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)Big Data Integration (2013)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda (2021)
this work references cited by second hop

References

  • 2002The Nonstochastic Multiarmed Bandit Problem2,210 cited
  • 1952Some aspects of the sequential design of experiments2,183 cited
  • 2009Data fusion1,848 cited
  • 2002Gambling in a rigged casino: The adversarial multi-armed bandit problem741 cited
  • 2006Improved second-order bounds for prediction with expert advice154 cited
  • 2005The max K-armed bandit: a new model of exploration applied to search heuristic selection114 cited
  • 2007Learning dynamic algorithm portfolios107 cited
  • 2006An asymptotically optimal algorithm for the max k-armed bandit problem77 cited
  • 2006Hannan Consistency in On-Line Learning in Case of Unbounded Losses Under Partial Monitoring40 cited

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