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The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints.
Counterspeculation, auctions, and competitive sealed tenders
William Vickrey · 1961
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Efficient mechanisms for bilateral trading
Roger B Myerson and Mark A Satterthwaite · 1983
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Tracking the best expert
Mark Herbster and Manfred K Warmuth · 1998
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Tracking a small set of experts by mixing past posteriors
Olivier Bousquet and Manfred K Warmuth · 2002
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Coordinating inventory control and pricing strategies with random demand and fixed ordering cost: The finite horizon case
Xin Chen and David Simchi-Levi · 2004
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Adaptive algorithms for online decision problems
Elad Hazan and Comandur Seshadhri · 2007
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Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms
Omar Besbes and Assaf Zeevi · 2009
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Dynamic pricing with limited supply
Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, and Aleksandrs Slivkins · 2012
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Learning on a budget: posted price mechanisms for online procurement
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Yaron Singer · 2012
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Mirror descent meets fixed share (and feels no regret)
Nicolò Cesa-Bianchi, Pierre Gaillard, Gábor Lugosi, and Gilles Stoltz · 2012
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Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2013
Cited alongside, same era.
Resourceful contextual bandits
Ashwinkumar Badanidiyuru, John Langford, and Aleksandrs Slivkins · 2014
Cited alongside, same era.
Close the gaps: A learning-while-doing algorithm for single-product revenue management problems
Zizhuo Wang, Shiming Deng, and Yinyu Ye · 2014
Cited alongside, same era.
Bandits with budgets: Regret lower bounds and optimal algorithms
Richard Combes, Chong Jiang, and Rayadurgam Srikant · 2015
Cited alongside, same era.
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Gergely Neu · 2015
Cited alongside, same era.
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
Shipra Agrawal, Nikhil R Devanur, and Lihong Li · 2016
The symmetry between arms and knapsacks: A primal-dual approach for bandits with knapsacks
Xiaocheng Li, Chunlin Sun, and Yinyu Ye · 2021
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The best of many worlds: Dual mirror descent for online allocation problems
Santiago R Balseiro, Haihao Lu, and Vahab Mirrokni · 2022
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Dynamic pricing and inventory control with fixed ordering cost and incomplete demand information
Boxiao Chen, David Simchi-Levi, Yining Wang, and Yuan Zhou · 2022
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Adversarial bandits with knapsacks
Nicole Immorlica, Karthik Sankararaman, Robert Schapire, and Aleksandrs Slivkins · 2022
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Non-monotonic resource utilization in the bandits with knapsacks problem
Raunak Kumar and Robert Kleinberg · 2022
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Cited alongside, same era.
Introduction to online convex optimization , volume 2
Elad Hazan et al · 2016
Cited alongside, same era.
Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2018
Cited alongside, same era.
Combinatorial semi-bandits with knapsacks
Karthik Abinav Sankararaman and Aleksandrs Slivkins · 2018
Cited alongside, same era.
Bandits with global convex constraints and objective
Shipra Agrawal and Nikhil R Devanur · 2019
Cited alongside, same era.
Online learning with knapsacks: the best of both worlds
Matteo Castiglioni, Andrea Celli, and Christian Kroer
Cited in the paper.
A unifying framework for online optimization with long-term constraints
Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Giulia Romano, and Nicola Gatti
Cited in the paper.
Matteo Castiglioni, Andrea Celli, and Christian Kroer · 2023
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Best of many worlds guarantees for online learning with knapsacks
Andrea Celli, Matteo Castiglioni, and Christian Kroer · 2023
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Bilateral trade: A regret minimization perspective
Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, and Stefano Leonardi · 2023
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Approximately stationary bandits with knapsacks
Giannis Fikioris and Éva Tardos · 2023
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