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Matroid Bandits: Fast Combinatorial Optimization with Learning
8 references·8 citing·31 second hop
Matroid Bandits: Fast Combinatorial Optimization with Learning (2014)Matroid Bandits: Fast Combinatorial Optim…From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning (2014)From Bandits to Monte-Carlo T…Regret in Online Combinatorial Optimization (2013)Regret in Online Combinatoria…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…Combinatorial bandits (2012)Combinatorial banditsLinear Submodular Bandits and their Application to Diversified Retrieval (2011)Linear Submodular Bandits and…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…Incorporating Behavioral Constraints in Online AI Systems (2019)Incorporating Behavioral Cons…Thompson Sampling for Combinatorial Semi-Bandits (2018)Thompson Sampling for Combina…Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (2018)Combinatorial Pure Exploratio…Stochastic Online Shortest Path Routing: The Value of Feedback (2017)Stochastic Online Shortest Pa…Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback (2016)Online Influence Maximization…DCM Bandits: Learning to Rank with Multiple Clicks (2016)DCM Bandits: Learning to Rank…Combinatorial Cascading Bandits (2015)Combinatorial Cascading Bandi…Efficient Learning in Large-Scale Combinatorial Semi-Bandits (2014)Efficient Learning in Large-S…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)The Nonstochastic Multiarmed Bandit Problem (2002)Optimal Best Arm Identification with Fixed Confidence (2016)Global optimization of Lipschitz functions (2017)Bandits and Experts in Metric Spaces (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)Learning diverse rankings with multi-armed bandits (2008)Contextual Bandits with Linear Payoff Functions (2011)Learning Hidden Features for Contextual Bandits (2016)An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment (2021)Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits (2023)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits (2014)Combinatorial Bandits Revisited (2015)Learning to Optimize via Information-Directed Sampling (2017)Some aspects of the sequential design of experiments (1952)Algorithmic Game Theory (2007)Improved Algorithms for Linear Stochastic Bandits (2011)Efficient algorithms for online decision problems (2004)From Bandits to Experts: On the Value of Side-Observations (2011)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)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
  • 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
  • 2012Combinatorial bandits320 cited
  • 2014From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning233 cited
  • 2013Regret in Online Combinatorial Optimization210 cited
  • 2011Linear Submodular Bandits and their Application to Diversified Retrieval101 cited

Cited by

  • 2016Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback74 cited
  • 2017Stochastic Online Shortest Path Routing: The Value of Feedback61 cited
  • 2018Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications60 cited
  • 2019Incorporating Behavioral Constraints in Online AI Systems59 cited
  • 2015Combinatorial Cascading Bandits58 cited
  • 2018Thompson Sampling for Combinatorial Semi-Bandits57 cited
  • 2016

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DCM Bandits: Learning to Rank with Multiple Clicks
52 cited
  • 2014Efficient Learning in Large-Scale Combinatorial Semi-Bandits48 cited