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Bandits with Global Convex Constraints and Objective
16 references·2 citing·38 second hop
Bandits with Global Convex Constraints and Objective (2019)Bandits with Global Convex Constraints an…Bandits with Knapsacks (2013)Bandits with KnapsacksRegret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Regret Analysis of Stochastic…Online Learning and Online Convex Optimization (2012)Online Learning and Online Co…Learning on a budget (2012)Learning on a budgetDynamic pricing with limited supply (2012)Dynamic pricing with limited …Blind Network Revenue Management (2012)Blind Network Revenue Managem…Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms (2009)Dynamic Pricing Without Knowi…Technical Note—A Risk-Sensitive Model for Managing Perishable Products (2008)Technical Note—A Risk-Sensiti…Multi-armed bandits in metric spaces (2008)Multi-armed bandits in metric…Approximation algorithms for budgeted learning problems (2007)Approximation algorithms for …Risk-sensitive capacity control in revenue management (2006)Risk-sensitive capacity contr…The Budgeted Multi-armed Bandit Problem (2004)The Budgeted Multi-armed Band…Additional Paper: EMSR versus EMSU: Revenue or utility? (2004)Additional Paper: EMSR versus…The financial risk of airline revenue management (2003)The financial risk of airline…Finite-time Analysis of the Multiarmed Bandit Problem (2002)Finite-time Analysis of the M…An analog of the minimax theorem for vector payoffs (1956)An analog of the minimax theo…Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing (2024)Network Revenue Management Wi…Applied Machine Learning in Operations Management (2021)Applied Machine Learning in O…Theory of Games and Economic Behavior (1945)Universal Portfolios (1991)Game theory, on-line prediction and boosting (1996)Adaptive Game Playing Using Multiplicative Weights (1999)Theory of games and economic behavior (2019)The value of knowing a demand curve: bounds on regret for online posted-price auctions (2004)Multi-parameter mechanism design and sequential posted pricing (2010)Incentivizing exploration (2014)Mechanism Design for Crowdsourcing: An Optimal 1-1/e Competitive Budget-Feasible Mechanism for Large Markets (2014)Resourceful Contextual Bandits (2014)Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons (1994)The Theory and Practice of Revenue Management (2004)Prediction, Learning, and Games (2006)Bandits with Knapsacks (2018)A review of revenue management: Recent generalizations and advances in industry applications (2019)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)The Nonstochastic Multiarmed Bandit Problem (2002)Bandits with concave rewards and convex knapsacks (2014)Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium (2017)Optimal Auction Design (1981)Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits (2015)Dynamic Pricing in High-Dimensions (2016)Bandits and Experts in Metric Spaces (2019)Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions (2003)Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies (2014)Dynamic pricing and learning: Historical origins, current research, and new directions (2015)Introduction to Multi-Armed Bandits (2019)Some aspects of the sequential design of experiments (1952)A general approach for predicting the behavior of the Supreme Court of the United States (2017)Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming (2019)Indexability of Restless Bandit Problems and Optimality of Whittle Index for Dynamic Multichannel Access (2010)Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization (2012)Safe Exploration for Optimization with Gaussian Processes (2015)Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting (2012)Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems (2012)Information-seeking, curiosity, and attention: computational and neural mechanisms (2013)A unified framework for stochastic optimization (2018)Bandit Algorithms (2020)
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References

  • 2002Finite-time Analysis of the Multiarmed Bandit Problem5,980 cited
  • 2012Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems1,275 cited
  • 2012Online Learning and Online Convex Optimization1,254 cited
  • 1956An analog of the minimax theorem for vector payoffs740 cited
  • 2009Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms510 cited
  • 2008Multi-armed bandits in metric spaces353 cited
  • 2013Bandits with Knapsacks211 cited
  • 2012Blind Network Revenue Management171 cited
  • 2012Learning on a budget113 cited
  • 2012Dynamic pricing with limited supply98 cited
  • 2007Approximation algorithms for budgeted learning problems85 cited
  • 2006Risk-sensitive capacity control in revenue management51 cited
  • 2004The Budgeted Multi-armed Bandit Problem44 cited
  • 2008Technical Note—A Risk-Sensitive Model for Managing Perishable Products40 cited
  • 2003The financial risk of airline revenue management37 cited
  • 2004Additional Paper: EMSR versus EMSU: Revenue or utility?

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30 cited

Cited by

  • 2021Applied Machine Learning in Operations Management28 cited
  • 2024Network Revenue Management With Demand Learning and Fair Resource-Consumption Balancing10 cited