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Restless multi-armed bandits (RMAB) is a framework for allocating limited resources under uncertainty.
The multi-armed bandit problem: decomposition and computation
Michael N Katehakis and Arthur F Veinott Jr · 1987
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On an index policy for restless bandits
Richard R Weber and Gideon Weiss · 1990
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Monitoring depression treatment outcomes with the patient health questionnaire-9
Bernd Löwe, Jürgen Unützer, Christopher M Callahan, Anthony J Perkins, and Kurt Kroenke · 2004
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Effectiveness of community health workers in the care of people with hypertension
J Nell Brownstein, Farah M Chowdhury, Susan L Norris, Tanya Horsley, Leonard Jack Jr, Xuanping Zhang, and Dawn Satterfield · 2007
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Adherence to continuous positive airway pressure therapy: the challenge to effective treatment
Terri E Weaver and Ronald R Grunstein · 2008
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House calls by community health workers and public health nurses to improve adherence to isoniazid monotherapy for latent tuberculosis infection: a retrospective study
Alicia H Chang, Andrea Polesky, and Gulshan Bhatia · 2013
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Markov models for treatment adherence in obstructive sleep apnea
Yuncheol Kang, Vittaldas V Prabhu, Amy M Sawyer, and Paul M Griffin · 2013
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The effects on tuberculosis treatment adherence from utilising community health workers: a comparison of selected rural and urban settings in kenya
Jane Rahedi Ong’ang’o, Christina Mwachari, Hillary Kipruto, and Simon Karanja · 2014
Earlier work this paper cites.
Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth · 2016
Earlier work this paper cites.
An alternative softmax operator for reinforcement learning
Kavosh Asadi and Michael L Littman · 2017
Cited alongside, same era.
An asymptotically optimal index policy for finite-horizon restless bandits
Weici Hu and Peter Frazier · 2017
Cited alongside, same era.
Fairness in reinforcement learning
Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth · 2017
Cited alongside, same era.
Restless bandits visiting villages: A preliminary study on distributing public health services
Biswarup Bhattacharya · 2018
Cited alongside, same era.
Reducing the risk of postpartum depression in a low-income community through a community health worker intervention
Christopher Mundorf, Arti Shankar, Tracy Moran, Sherry Heller, Anna Hassan, Emily Harville, and Maureen Lichtveld · 2018
Cited alongside, same era.
Theoretical analysis of efficiency and robustness of softmax and gap-increasing operators in reinforcement learning
Tadashi Kozuno, Eiji Uchibe, and Kenji Doya · 2019
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Optimal screening for hepatocellular carcinoma: A restless bandit model
Elliot Lee, Mariel S Lavieri, and Michael Volk · 2019
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Combinatorial sleeping bandits with fairness constraints
Fengjiao Li, Jia Liu, and Bo Ji · 2019
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Revisiting the softmax bellman operator: New benefits and new perspective
Zhao Song, Ron Parr, and Lawrence Carin · 2019
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Collapsing bandits and their application to public health interventions
Aditya Mate, Jackson A Killian, Haifeng Xu, Andrew Perrault, and Milind Tambe · 2020
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Community health workers improve disease control and medication adherence among patients with diabetes and/or hypertension in chiapas, mexico: an observational stepped-wedge study
Patrick M Newman, Molly F Franke, Jafet Arrieta, Hector Carrasco, Patrick Elliott, Hugo Flores, Alexandra Friedman, Sophia Graham, Luis Martinez, Lindsay Palazuelos, et al · 2018
Cited alongside, same era.
Ensuring fairness in machine learning to advance health equity
Alvin Rajkomar, Michaela Hardt, Michael D Howell, Greg Corrado, and Marshall H Chin · 2018
Cited alongside, same era.
Restless bandits with controlled restarts: Indexability and computation of whittle index
Nima Akbarzadeh and Aditya Mahajan · 2019
Cited alongside, same era.
Key challenges for delivering clinical impact with artificial intelligence
Christopher J Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King · 2019
Cited alongside, same era.
Achieving fairness in the stochastic multi-armed bandit problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair, and Y. Narahari · 2020
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Planning to fairly allocate: Probabilistic fairness in the restless bandit setting
Christine Herlihy, Aviva Prins, Aravind Srinivasan, and John Dickerson · 2021
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Efficient algorithms for finite horizon and streaming restless multi-armed bandit problems
Aditya Mate, Arpita Biswas, Christoph Siebenbrunner, and Milind Tambe · 2021
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Efficient resource allocation with fairness constraints in restless multi-armed bandits
Dexun Li and Pradeep Varakantham · 2022
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