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Found 5 papers, 0 papers with code
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally.
Online learning in bandits with predicted context
When the context error is non-vanishing, classical bandit algorithms fail to achieve sublinear regret.
Policy Optimization Using Semi-parametric Models for Dynamic Pricing
$F(\cdot)$ with $m$-th order derivative ($m\geq 2$), our policy achieves a regret upper bound of $\tilde{O}_{d}(T^{\frac{2m+1}{4m-1}})$, where $T$ is time horizon and $\tilde{O}_{d}$ is the order that hides logarithmic terms and the dimensionality of feature $d$.
Machine Learning for Variance Reduction in Online Experiments
We consider the problem of variance reduction in randomized controlled trials, through the use of covariates correlated with the outcome but independent of the treatment.
Communication-Efficient Accurate Statistical Estimation
In addition, we give the conditions under which the one-step CEASE estimator is statistically efficient.
