Fetching the paper…
Reading the bibliography…
The linear contextual bandit literature is mostly focused on the design of efficient learning algorithms for a given representation.
The non-singularity of generalized sample covariance matrices
Morris L Eaton and Michael D Perlman · 1973
Earlier work this paper cites.
Eigentaste: A constant time collaborative filtering algorithm
Kenneth Y. Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins · 2001
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
Earlier work this paper cites.
Inequalities on the lambert w function and hyperpower function
Abdolhossein Hoorfar and Mehdi Hassani · 2008
Earlier work this paper cites.
Nonparametric bandits with covariates
Philippe Rigollet and Assaf Zeevi · 2010
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011)
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik · 2011
Earlier work this paper cites.
Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert E. Schapire · 2011
Earlier work this paper cites.
Adaptive bandits: Towards the best history-dependent strategy
Odalric-Ambrym Maillard and Rémi Munos · 2011
Earlier work this paper cites.
Online-to-confidence-set conversions and application to sparse stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2012
Earlier work this paper cites.
User-friendly tail bounds for sums of random matrices
Joel A. Tropp · 2012
Earlier work this paper cites.
A note on matrix versions of kantorovich-type inequality
Feixiang Chen · 2013
Earlier work this paper cites.
Explore no more: Improved high-probability regret bounds for non-stochastic bandits
Gergely Neu · 2015
Cited alongside, same era.
Bounds on the lambert function and their application to the outage analysis of user cooperation
Ioannis Chatzigeorgiou · 2016
Cited alongside, same era.
Linear thompson sampling revisited
Marc Abeille and Alessandro Lazaric · 2017
Cited alongside, same era.
Corralling a band of bandit algorithms
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, and Robert E. Schapire · 2017
Cited alongside, same era.
The end of optimism? an asymptotic analysis of finite-armed linear bandits
Tor Lattimore and Csaba Szepesvari · 2017
Cited alongside, same era.
The k-nearest neighbour UCB algorithm for multi-armed bandits with covariates
Henry W. J. Reeve, Joe Mellor, and Gavin Brown · 2018
OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits
Niladri S. Chatterji, Vidya Muthukumar, and Peter L. Bartlett · 2020
Later among the works it cites.
Gamification of pure exploration for linear bandits
Rémy Degenne, Pierre Ménard, Xuedong Shang, and Michal Valko · 2020
Later among the works it cites.
Adapting to misspecification in contextual bandits
Dylan J. Foster, Claudio Gentile, Mehryar Mohri, and Julian Zimmert · 2020
Later among the works it cites.
Problem-complexity adaptive model selection for stochastic linear bandits
Avishek Ghosh, Abishek Sankararaman, and Kannan Ramchandran · 2020
Later among the works it cites.
Adaptive exploration in linear contextual bandit
Botao Hao, Tor Lattimore, and Csaba Szepesvári · 2020
Later among the works it cites.
Provably efficient reinforcement learning with linear function approximation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A survey on practical applications of multi-armed and contextual bandits, 2019
Djallel Bouneffouf and Irina Rish · 2019
Cited alongside, same era.
Model selection for contextual bandits
Dylan J. Foster, Akshay Krishnamurthy, and Haipeng Luo · 2019
Cited alongside, same era.
Regret balancing for bandit and rl model selection, 2020
Yasin Abbasi-Yadkori, Aldo Pacchiano, and My Phan · 2020
Cited alongside, same era.
Corralling stochastic bandit algorithms
Raman Arora, Teodor V. Marinov, and Mehryar Mohri · 2020
Cited alongside, same era.
Mostly exploration-free algorithms for contextual bandits
Hamsa Bastani, Mohsen Bayati, and Khashayar Khosravi · 2020
Cited alongside, same era.
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms
Aurélien F. Bibaut, Antoine Chambaz, and Mark J. van der Laan · 2020
Cited alongside, same era.
Chi Jin, Zhuoran Yang, Zhaoran Wang, and Michael I. Jordan · 2020
Later among the works it cites.
Bandit algorithms
Tor Lattimore and Csaba Szepesvári · 2020
Later among the works it cites.
Learning with good feature representations in bandits and in rl with a generative model
Tor Lattimore, Csaba Szepesvari, and Gellert Weisz · 2020
Later among the works it cites.
Online model selection for reinforcement learning with function approximation, 2020
Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, and Emma Brunskill · 2020
Later among the works it cites.
An asymptotically optimal primal-dual incremental algorithm for contextual linear bandits
Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, and Alessandro Lazaric · 2020
Later among the works it cites.
Stochastic linear contextual bandits with diverse contexts
Weiqiang Wu, Jing Yang, and Cong Shen · 2020
Later among the works it cites.