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Informational confidence bounds for self-normalized averages and applications
13 references·2 citing·28 second hop
Informational confidence bounds for self-normalized averages and applications (2013)Informational confidence bounds for self-…Kullback–Leibler upper confidence bounds for optimal sequential allocation (2013)Kullback–Leibler upper confid…KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION (2013)KULLBACK-LEIBLER UPPER CONFID…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond (2011)The KL-UCB Algorithm for Boun…On Upper-Confidence Bound Policies for Switching Bandit Problems (2011)On Upper-Confidence Bound Pol…Improved Algorithms for Linear Stochastic Bandits (2011)Improved Algorithms for Linea…Parametric Bandits: The Generalized Linear Case (2010)Parametric Bandits: The Gener…Exploration–exploitation tradeoff using variance estimates in multi-armed bandits (2009)Exploration–exploitation trad…On Upper-Confidence Bound Policies for Non-Stationary Bandit Problems (2008)On Upper-Confidence Bound Pol…Minimizing Regret With Label Efficient Prediction (2005)Minimizing Regret With Label …Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Sample mean based index policies byO(logn) regret for the multi-armed bandit problem (1995)Sample mean based index polic…Conservative Bandits (2016)Conservative BanditsRegret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits (2015)Regret Analysis of the Finite…Bandit Processes and Dynamic Allocation Indices (1979)Optimistic bayesian sampling in contextual-bandit problems (2012)Bandits With Heavy Tail (2013)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Prediction, Learning, and Games (2006)Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis (2012)Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards (2014)Chasing Demand: Learning and Earning in a Changing Environment (2016)A Stochastic Approximation Method (1951)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Introduction to Multi-Armed Bandits (2019)Some aspects of the sequential design of experiments (1952)Thompson Sampling for Contextual Bandits with Linear Payoffs (2012)Learning to Optimize via Posterior Sampling (2014)Multi-Armed Bandit Allocation Indices. (1990)Intrinsic motivation, curiosity, and learning (2016)Using confidence bounds for exploitation-exploration trade-offs (2003)From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)Cascading Bandits: Learning to Rank in the Cascade Model (2015)Linearly Parameterized Bandits (2010)Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2011)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Information-seeking, curiosity, and attention: computational and neural mechanisms (2013)A unified framework for stochastic optimization (2018)Bandit Algorithms (2020)Batched bandit problems (2016)Explore First, Exploit Next: The True Shape of Regret in Bandit Problems (2016)
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References

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  • 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems1,410 cited
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  • 2011Improved Algorithms for Linear Stochastic Bandits860 cited
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  • 2011On Upper-Confidence Bound Policies for Switching Bandit Problems455 cited
  • 2011The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond363 cited
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Cited by

  • 2015Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits68 cited
  • 2016

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Conservative Bandits
42 cited