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Found 7 papers, 1 papers with code
TransformerLSR: Attentive Joint Model of Longitudinal Data, Survival, and Recurrent Events with Concurrent Latent Structure
However, current methods only address joint modeling of longitudinal measurements at regularly-spaced observation times and survival events, neglecting recurrent events.
MoMA: Model-based Mirror Ascent for Offline Reinforcement Learning
Model-based offline reinforcement learning methods (RL) have achieved state-of-the-art performance in many decision-making problems thanks to their sample efficiency and generalizability.
A Policy Gradient Method for Confounded POMDPs
To the best of our knowledge, this is the first work studying the policy gradient method for POMDPs under the offline setting.
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation
This paper presents a computational framework that generates ensemble predictive mechanics models with uncertainty quantification (UQ).
Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing
In the second step, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations learned from the posterior inference of the Bayesian joint model in the first step.
Methodology
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner.
