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In the bandits with knapsacks framework (BwK) the learner has $m$ resource-consumption (packing) constraints.
A game of prediction with expert advice
Vladimir G Vovk · 1995
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Relative loss bounds for on-line density estimation with the exponential family of distributions
Katy S Azoury and Manfred K Warmuth · 2001
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
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Online convex programming and generalized infinitesimal gradient ascent
Martin Zinkevich · 2003
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Adaptive algorithms for online decision problems
Elad Hazan and Comandur Seshadhri · 2007
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A contextual-bandit approach to personalized news article recommendation
Lihong Li, Wei Chu, John Langford, and Robert E Schapire · 2010
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
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Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2013
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Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Bandits with concave rewards and convex knapsacks
Shipra Agrawal and Nikhil R Devanur · 2014
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Resourceful contextual bandits
Ashwinkumar Badanidiyuru, John Langford, and Aleksandrs Slivkins · 2014
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Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Gergely Neu · 2015
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Linear contextual bandits with knapsacks
Shipra Agrawal and Nikhil Devanur · 2016
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An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
Shipra Agrawal, Nikhil R Devanur, and Lihong Li · 2016
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Introduction to online convex optimization
Elad Hazan et al · 2016
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Online convex optimization with stochastic constraints
Hao Yu, Michael Neely, and Xiaohan Wei · 2017
Cited alongside, same era.
Bandits with knapsacks
Ashwinkumar Badanidiyuru, Robert Kleinberg, and Aleksandrs Slivkins · 2018
Cited alongside, same era.
Practical contextual bandits with regression oracles
Dylan Foster, Alekh Agarwal, Miroslav Dudík, Haipeng Luo, and Robert Schapire · 2018
Cited alongside, same era.
Scale-free online learning
Francesco Orabona and Dávid Pál · 2018
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Bandits with global convex constraints and objective
Shipra Agrawal and Nikhil R Devanur · 2019
Cited alongside, same era.
Learning in repeated auctions with budgets: Regret minimization and equilibrium
Santiago R Balseiro and Yonatan Gur · 2019
Cited alongside, same era.
Non-monotonic resource utilization in the bandits with knapsacks problem
Raunak Kumar and Robert Kleinberg · 2022
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Non-stationary bandits with knapsacks
Shang Liu, Jiashuo Jiang, and Xiaocheng Li · 2022
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Bypassing the monster: A faster and simpler optimal algorithm for contextual bandits under realizability
David Simchi-Levi and Yunzong Xu · 2022
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Online resource allocation under horizon uncertainty
Santiago Balseiro, Christian Kroer, and Rachitesh Kumar · 2023
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Bandits with replenishable knapsacks: the best of both worlds
Martino Bernasconi, Matteo Castiglioni, Andrea Celli, and Federico Fusco · 2023
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Online bidding in repeated non-truthful auctions under budget and ROI constraints
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Adversarial bandits with knapsacks
Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, and Aleksandrs Slivkins · 2019
Cited alongside, same era.
Introduction to multi-armed bandits
Aleksandrs Slivkins et al · 2019
Cited alongside, same era.
Beyond ucb: Optimal and efficient contextual bandits with regression oracles
Dylan Foster and Alexander Rakhlin · 2020
Cited alongside, same era.
Online learning with vector costs and bandits with knapsacks
Thomas Kesselheim and Sahil Singla · 2020
Cited alongside, same era.
Online primal-dual mirror descent under stochastic constraints
Xiaohan Wei, Hao Yu, and Michael J Neely · 2020
Cited alongside, same era.
A contextual bandit bake-off
Alberto Bietti, Alekh Agarwal, and John Langford · 2021
Cited alongside, same era.
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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Online bidding algorithms for return-on-spend constrained advertisers
Zhe Feng, Swati Padmanabhan, and Di Wang · 2023
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Approximately stationary bandits with knapsacks
Giannis Fikioris and Éva Tardos · 2023
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Budget pacing in repeated auctions: Regret and efficiency without convergence
Jason Gaitonde, Yingkai Li, Bar Light, Brendan Lucier, and Aleksandrs Slivkins · 2023
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Optimal contextual bandits with knapsacks under realizability via regression oracles
Yuxuan Han, Jialin Zeng, Yang Wang, Yang Xiang, and Jiheng Zhang · 2023
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Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression
Aleksandrs Slivkins, Karthik Abinav Sankararaman, and Dylan J Foster · 2023
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Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression
Aleksandrs Slivkins, Karthik Abinav Sankararaman, and Dylan J. Foster · 2023
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Learning to bid in repeated first-price auctions with budgets
Qian Wang, Zongjun Yang, Xiaotie Deng, and Yuqing Kong · 2023
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No-regret learning in bilateral trade via global budget balance
Martino Bernasconi, Matteo Castiglioni, Andrea Celli, and Federico Fusco · 2024
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