SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
4 references·16 citing·32 second hop
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond (2011)The KL-UCB Algorithm for Bounded Stochast…Exploration–exploitation tradeoff using variance estimates in multi-armed bandits (2009)Exploration–exploitation trad…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…Bandit Processes and Dynamic Allocation Indices (1979)Bandit Processes and Dynamic …Bandits and Experts in Metric Spaces (2019)Bandits and Experts in Metric…Stochastic bandits robust to adversarial corruptions (2018)Stochastic bandits robust to …On Bayesian index policies for sequential resource allocation (2018)On Bayesian index policies fo…Near-Optimal Regret Bounds for Thompson Sampling (2017)Near-Optimal Regret Bounds fo…Minimal Exploration in Structured Stochastic Bandits (2017)Minimal Exploration in Struct…Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems (2016)Adaptive Contract Design for …Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits (2014)Modeling Human Decision Makin…Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms (2014)Combinatorial Multi-Armed Ban…Online Learning under Delayed Feedback (2013)Online Learning under Delayed…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…Informational confidence bounds for self-normalized averages and applications (2013)Informational confidence boun…Bandits With Heavy Tail (2013)Bandits With Heavy TailOptimistic bayesian sampling in contextual-bandit problems (2012)Optimistic bayesian sampling …Online Learning of Rested and Restless Bandits (2012)Online Learning of Rested and…1 1 Online Learning of Rested and Restless Bandits (2012)1 1 Online Learning of Rested…Prediction, Learning, and Games (2006)From Ads to Interventions: Contextual Bandits in Mobile Health (2017)Linear Thompson sampling revisited (2017)MNL-Bandit: A Dynamic Learning Approach to Assortment Selection (2019)Some aspects of the sequential design of experiments (1952)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Risk-Averse Multi-Armed Bandit Problems Under Mean-Variance Measure (2016)A/B Testing with Fat Tails (2020)Multi-Armed Bandit Allocation Indices. (1990)Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis (2012)Collaborative Filtering Bandits (2016)Intrinsic motivation, curiosity, and learning (2016)Bandit Problems, Sequential Allocation of Experiments. (1987)The Nonstochastic Multiarmed Bandit Problem (2002)Cascading Bandits: Learning to Rank in the Cascade Model (2015)Combinatorial Multi-Armed Bandit with General Reward Functions (2016)Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback (2016)Applied Statistical Decision Theory (1962)Applied Statistical Decision Theory. (1962)Restless bandits: activity allocation in a changing world (1988)Legislative leviathan: party government in the House (1993)Linearly Parameterized Bandits (2010)On Upper-Confidence Bound Policies for Switching Bandit Problems (2011)Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms (2011)Batched bandit problems (2016)Learning to Optimize via Information-Directed Sampling (2017)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)Explore First, Exploit Next: The True Shape of Regret in Bandit Problems (2016)Learning in a Changing World: Restless Multiarmed Bandit With Unknown Dynamics (2012)Deterministic Sequencing of Exploration and Exploitation for Multi-Armed Bandit Problems (2013)On Regret-Optimal Learning in Decentralized Multiplayer Multiarmed Bandits (2016)
this work references cited by second hop

References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 1979Bandit Processes and Dynamic Allocation Indices1,488 cited
  • 2009Exploration–exploitation tradeoff using variance estimates in multi-armed bandits571 cited
  • 1995Sample mean based index policies byO(logn) regret for the multi-armed bandit problem539 cited

Cited by

  • 2013KULLBACK-LEIBLER UPPER CONFIDENCE BOUNDS FOR OPTIMAL SEQUENTIAL ALLOCATION274 cited
  • 2013Bandits With Heavy Tail249 cited
  • 2012Optimistic bayesian sampling in contextual-bandit problems213 cited
  • 2014Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms196 cited
  • 2013Kullback–Leibler upper confidence bounds for optimal sequential allocation181 cited
  • 20121 1 Online Learning of Rested and Restless Bandits141 cited
  • 2017Near-Optimal Regret Bounds for Thompson Sampling118 cited
  • 2012Online Learning of Rested and Restless Bandits114 cited
  • 2017Minimal Exploration in Structured Stochastic Bandits113 cited
  • 2018Stochastic bandits robust to adversarial corruptions111 cited
  • 2018On Bayesian index policies for sequential resource allocation

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

97 cited
  • 2014Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits80 cited
  • 2013Online Learning under Delayed Feedback78 cited
  • 2019Bandits and Experts in Metric Spaces75 cited
  • 2016Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems75 cited
  • 2013Informational confidence bounds for self-normalized averages and applications74 cited