Deep spectral Q-learning with application to mobile health

Cites Work

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Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy
From sparse to dense functional data and beyond
Optimal eigen expansions and uniform bounds
Performance guarantees for individualized treatment rules
Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
Fast learning rates for plug-in classifiers
D-learning to estimate optimal individual treatment rules
Optimal aggregation of classifiers in statistical learning.
Nonasymptotic upper bounds for the reconstruction error of PCA
Nonparametric regression using deep neural networks with ReLU activation function
Asymptotics of prediction in functional linear regression with functional outputs
Concordance and value information criteria for optimal treatment decision
Treatment decisions based on scalar and functional baseline covariates
Likelihood Ratio Tests for Dependent Data with Applications to Longitudinal and Functional Data Analysis
Penalized Q-learning for dynamic treatment regimens
Distribution of the Estimators for Autoregressive Time Series With a Unit Root
Interpretable Dynamic Treatment Regimes
Testing for unit roots in autoregressive-moving average models of unknown order
Functional Feature Construction for Individualized Treatment Regimes
Constructing dynamic treatment regimes over indefinite time horizons
Optimal Dynamic Treatment Regimes
Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects
Learning Optimal Distributionally Robust Individualized Treatment Rules
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning
Bayesian Nonparametric Policy Search With Application to Periodontal Recall Intervals
Estimating Dynamic Treatment Regimes in Mobile Health Using V-Learning
Estimation of treatment policies based on functional predictors
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
Selecting the Number of Principal Components in Functional Data
Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
On-Line Estimation with the Multivariate Gaussian Distribution
Functional Data Analysis for Sparse Longitudinal Data
Prediction by Supervised Principal Components
Learning When-to-Treat Policies
Robust Q-Learning
Off-Policy Estimation of Long-Term Average Outcomes With Applications to Mobile Health
Flexible functional regression methods for estimating individualized treatment rules
Statistical inference of the value function for reinforcement learning in infinite-horizon settings
Constructing treatment decision rules based on scalar and functional predictors when moderators of treatment effect are unknown

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