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Found 5 papers, 2 papers with code
Practical Policy Optimization with Personalized Experimentation
Many organizations measure treatment effects via an experimentation platform to evaluate the casual effect of product variations prior to full-scale deployment.
Fast Rates for the Regret of Offline Reinforcement Learning
Second, we provide new analyses of FQI and Bellman residual minimization to establish the correct pointwise convergence guarantees.
Fast Rates for Contextual Linear Optimization
While one may use off-the-shelf machine learning methods to separately learn a predictive model and plug it in, a variety of recent methods instead integrate estimation and optimization by fitting the model to directly optimize downstream decision performance.
DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret
While existing literature mostly focuses on estimating the optimal DTR from offline data such as from sequentially randomized trials, we study the problem of developing the optimal DTR in an online manner, where the interaction with each individual affect both our cumulative reward and our data collection for future learning.
Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret Regimes
We study a nonparametric contextual bandit problem where the expected reward functions belong to a H\"older class with smoothness parameter $\beta$.
