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On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems
13 references·1 citing·35 second hopsampled
On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems (2018)On Abruptly-Changing and Slowly-Varying M…Optimal Exploration–Exploitation in a Multi-armed Bandit Problem with Non-stationary Rewards (2019)Optimal Exploration–Exploitat…A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problem (2018)A Change-Detection Based Fram…On Regret-Optimal Learning in Decentralized Multiplayer Multiarmed Bandits (2016)On Regret-Optimal Learning in…Surveillance in an abruptly changing world via multiarmed bandits (2014)Surveillance in an abruptly c…Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits (2014)Modeling Human Decision Makin…Stochastic surveillance strategies for spatial quickest detection (2013)Stochastic surveillance strat…On optimal foraging and multi-armed bandits (2013)On optimal foraging and multi…Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems (2013)Deterministic Sequencing of E…Learning in a Changing World: Restless Multiarmed Bandit With Unknown Dynamics (2012)Learning in a Changing World:…On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems (2008)On Upper-Confidence Bound Pol…The Nonstochastic Multiarmed Bandit Problem (2002)The Nonstochastic Multiarmed …Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Asymptotically efficient adaptive allocation rules for the multiarmed bandit problem with switching cost (1988)Asymptotically efficient adap…Hedging the Drift: Learning to Optimize under Non-Stationarity (2019)Hedging the Drift: Learning t…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Prediction, Learning, and Games (2006)Hedging the Drift: Learning to Optimize Under Non-Stationarity (2018)A Simple Approach for Non-stationary Linear Bandits (2021)Nonstationary Reinforcement Learning: The Blessing of (More) Optimism (2023)Some aspects of the sequential design of experiments (1952)A Simple Adaptive Procedure Leading to Correlated Equilibrium (2000)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Taking the Human Out of the Loop: A Review of Bayesian Optimization (2015)Sequential Analysis (1985)Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I.I.D. rewards (1987)Optimal learning and experimentation in bandit problems (2002)Sequential Learning for Multi-Channel Wireless Network Monitoring With Channel Switching Costs (2014)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Human Supervisory Control of Robotic Teams: Integrating Cognitive Modeling with Engineering Design (2015)On distributed cooperative decision-making in multiarmed bandits (2016)Finding structure in multi-armed bandits (2018)Bandit Processes and Dynamic Allocation Indices (1979)Channel Selection for Network-Assisted D2D Communication via No-Regret Bandit Learning With Calibrated Forecasting (2014)Multi-armed bandits with application to 5G small cells (2016)Multi-Armed Bandit Allocation Indices (1989)Multi-Armed Bandit Allocation Indices. (1990)Sample mean based index policies by O(log n) regret for the multi-armed bandit problem (1995)CONTINUOUS INSPECTION SCHEMES (1954)Learning Contextual Bandits in a Non-stationary Environment (2018)A New Algorithm for Non-stationary Contextual Bandits: Efficient, Optimal, and Parameter-free (2019)An Empirical Evaluation of Thompson Sampling (2011)Collaborative Learning of Stochastic Bandits Over a Social Network (2016)A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players (2019)Distributed Learning for Channel Allocation Over a Shared Spectrum (2019)Near-optimal Regret Bounds for Reinforcement Learning (2010)Improved Algorithms for Linear Stochastic Bandits (2011)Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms (2014)Risk-Averse Multi-Armed Bandit Problems Under Mean-Variance Measure (2016)Multi-armed bandits in multi-agent networks (2017)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem6,033 cited
  • 2002The Nonstochastic Multiarmed Bandit Problem2,229 cited
  • 2008On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems172 cited
  • 2012Learning in a Changing World: Restless Multiarmed Bandit With Unknown Dynamics139 cited
  • 1988Asymptotically efficient adaptive allocation rules for the multiarmed bandit problem with switching cost112 cited
  • 2019Optimal Exploration–Exploitation in a Multi-armed Bandit Problem with Non-stationary Rewards101 cited
  • 2018A Change-Detection Based Framework for Piecewise-Stationary Multi-Armed Bandit Problem95 cited
  • 2013Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems90 cited
  • 2014Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits80 cited
  • 2016On Regret-Optimal Learning in Decentralized Multiplayer Multiarmed Bandits66 cited
  • 2013Stochastic surveillance strategies for spatial quickest detection35 cited
  • 2014Surveillance in an abruptly changing world via multiarmed bandits34 cited
  • 2013On optimal foraging and multi-armed bandits34 cited

Cited by

  • 2019Hedging the Drift: Learning to Optimize under Non-Stationarity29 cited

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