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Found 7 papers, 0 papers with code
Offline Policy Evaluation and Optimization under Confounding
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning.
Bandit Algorithms for Precision Medicine
However, the modern precision medicine movement has been enabled by a confluence of events: scientific advances in fields such as genetics and pharmacology, technological advances in mobile devices and wearable sensors, and methodological advances in computing and data sciences.
Causal Bandits with Unknown Graph Structure
In causal bandit problems, the action set consists of interventions on variables of a causal graph.
Causal Markov Decision Processes: Learning Good Interventions Efficiently
We introduce causal Markov Decision Processes (C-MDPs), a new formalism for sequential decision making which combines the standard MDP formulation with causal structures over state transition and reward functions.
Low-Rank Generalized Linear Bandit Problems
To get around the computational intractability of covering based approaches, we propose an efficient algorithm by extending the "Explore-Subspace-Then-Refine" algorithm of~\citet{jun2019bilinear}.
Regret Analysis of Bandit Problems with Causal Background Knowledge
For example, we observe that even with a few hundreds of iterations, the regret of causal algorithms is less than that of standard algorithms by a factor of three.
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions
Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions.
