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Combinatorial Sleeping Bandits with Fairness Constraints
12 references·2 citing·33 second hop
Combinatorial Sleeping Bandits with Fairness Constraints (2019)Combinatorial Sleeping Bandits with Fairn…Bandits with Knapsacks (2018)Bandits with KnapsacksFairness in learning: classic and contextual bandits (2016)Fairness in learning: classic…Fairness in Learning: Classic and Contextual Bandits (2016)Fairness in Learning: Classic…Combinatorial Bandits Revisited (2015)Combinatorial Bandits Revisit…Combinatorial Multi-Armed Bandit: General Framework and Applications (2013)Combinatorial Multi-Armed Ban…Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations (2012)Combinatorial Network Optimiz…Multi‐Armed Bandit Allocation Indices (2011)Multi‐Armed Bandit Allocation…Regret bounds for sleeping experts and bandits (2010)Regret bounds for sleeping ex…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Multi-Armed Bandit Allocation Indices. (1990)Multi-Armed Bandit Allocation…Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I.I.D. rewards (1987)Asymptotically efficient allo…Fairness in Learning-Based Sequential Decision Algorithms: A Survey (2021)Fairness in Learning-Based Se…Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates (2020)Multi-Armed Bandits with Fair…Some aspects of the sequential design of experiments (1952)The Nonstochastic Multiarmed Bandit Problem (2002)Mortal Multi-Armed Bandits (2008)An Asymptotically Optimal Bandit Algorithm for Bounded Support Models. (2010)Multi-armed bandits with application to 5G small cells (2016)A Stochastic Approximation Method (1951)Prediction, Learning, and Games (2006)Introduction to Online Convex Optimization (2016)Introduction to Online Convex Optimization (2016)Introduction to Multi-Armed Bandits (2019)Using confidence bounds for exploitation-exploration trade-offs (2003)Stochastic Linear Optimization Under Bandit Feedback (2008)Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms (2014)Decentralized Learning for Multiplayer Multiarmed Bandits (2014)Cascading Bandits: Learning to Rank in the Cascade Model (2015)Fairness through awareness (2012)Big Data's Disparate Impact (2014)Algorithmic fairness in business analytics: Directions for research and practice (2022)Algorithmic Fairness (2023)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Bandits and Experts in Metric Spaces (2019)Combinatorial Sleeping Bandits With Fairness Constraints (2019)Feature-Based Dynamic Pricing (2020)The Continuum-Armed Bandit Problem (1995)Indexability of Restless Bandit Problems and Optimality of Whittle Index for Dynamic Multichannel Access (2010)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)An Empirical Evaluation of Thompson Sampling (2011)Analysis of Thompson Sampling for the multi-armed bandit problem (2011)Learning to Optimize via Posterior Sampling (2014)A Tutorial on Thompson Sampling (2018)A unified framework for stochastic optimization (2018)Combinatorial Pure Exploration of Multi-Armed Bandits (2014)Efficient learning by implicit exploration in bandit problems with side observations (2026)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems1,410 cited
  • 1990Multi-Armed Bandit Allocation Indices.997 cited
  • 2011Multi‐Armed Bandit Allocation Indices685 cited
  • 2012Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations413 cited
  • 2013Combinatorial Multi-Armed Bandit: General Framework and Applications392 cited
  • 1987Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays-Part I: I.I.D. rewards358 cited
  • 2010Regret bounds for sleeping experts and bandits200 cited
  • 2016Fairness in Learning: Classic and Contextual Bandits199 cited
  • 2018Bandits with Knapsacks137 cited
  • 2015Combinatorial Bandits Revisited124 cited
  • 2016Fairness in learning: classic and contextual bandits95 cited

Cited by

  • 2020Multi-Armed Bandits with Fairness Constraints for Distributing Resources to Human Teammates44 cited
  • 2021Fairness in Learning-Based Sequential Decision Algorithms: A Survey41 cited

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