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Identifying Best Interventions through Online Importance Sampling

Identifying Best Interventions through Online Importance Sampling

10 January 2017
Rajat Sen
Karthikeyan Shanmugam
A. Dimakis
Sanjay Shakkottai
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Papers citing "Identifying Best Interventions through Online Importance Sampling"

6 / 6 papers shown
Title
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction
  to Linear Bandits, and Limitations around Unknown Marginals
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Ziyi Liu
Idan Attias
Daniel M. Roy
CML
27
0
0
01 Jul 2024
Optimal Observation-Intervention Trade-Off in Optimisation Problems with
  Causal Structure
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
K. Hammar
Neil Dhir
CML
10
0
0
05 Sep 2023
Causal Bandits without Graph Learning
Causal Bandits without Graph Learning
Mikhail Konobeev
Jalal Etesami
Negar Kiyavash
CML
13
8
0
26 Jan 2023
Bandit Algorithms for Precision Medicine
Bandit Algorithms for Precision Medicine
Yangyi Lu
Ziping Xu
Ambuj Tewari
53
11
0
10 Aug 2021
Causal Markov Decision Processes: Learning Good Interventions
  Efficiently
Causal Markov Decision Processes: Learning Good Interventions Efficiently
Yangyi Lu
A. Meisami
Ambuj Tewari
18
10
0
15 Feb 2021
Budgeted and Non-budgeted Causal Bandits
Budgeted and Non-budgeted Causal Bandits
V. Nair
Vishakha Patil
Gaurav Sinha
13
41
0
13 Dec 2020
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