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Challenges in Statistical Analysis of Data Collected by a Bandit
  Algorithm: An Empirical Exploration in Applications to Adaptively Randomized
  Experiments

Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments

22 March 2021
Joseph Jay Williams
Jacob Nogas
Nina Deliu
Hammad Shaikh
S. Villar
A. Durand
Anna N. Rafferty
    AAML
ArXivPDFHTML

Papers citing "Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments"

5 / 5 papers shown
Title
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions
Omer Noy Klein
Alihan Huyuk
Ron Shamir
Uri Shalit
M. Schaar
FaML
55
0
0
13 Mar 2025
Opportunities for Adaptive Experiments to Enable Continuous Improvement
  in Computer Science Education
Opportunities for Adaptive Experiments to Enable Continuous Improvement in Computer Science Education
Ilya Musabirov
Angela M. Zavaleta Bernuy
Pan Chen
Michael Liut
Joseph Jay Williams
19
1
0
18 Oct 2023
Using Adaptive Bandit Experiments to Increase and Investigate Engagement
  in Mental Health
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health
Harsh Kumar
Tong Li
Jiakai Shi
Ilya Musabirov
Rachel Kornfield
...
David C. Mohr
Anna N. Rafferty
Sofia S. Villar
Nina Deliu
Joseph Jay Williams
23
6
0
13 Oct 2023
Infinite Action Contextual Bandits with Reusable Data Exhaust
Infinite Action Contextual Bandits with Reusable Data Exhaust
Mark Rucker
Yinglun Zhu
Paul Mineiro
OffRL
21
1
0
16 Feb 2023
Multi-disciplinary fairness considerations in machine learning for
  clinical trials
Multi-disciplinary fairness considerations in machine learning for clinical trials
Isabel Chien
Nina Deliu
Richard Turner
Adrian Weller
S. Villar
Niki Kilbertus
FaML
39
20
0
18 May 2022
1