Web: http://arxiv.org/abs/2209.07148

Sept. 16, 2022, 1:11 a.m. | Gholamali Aminian, Roberto Vega, Omar Rivasplata, Laura Toni, Miguel Rodrigues

cs.LG updates on arXiv.org arxiv.org

Counterfactual risk minimization is a framework for offline policy
optimization with logged data which consists of context, action, propensity
score, and reward for each sample point. In this work, we build on this
framework and propose a learning method for settings where the rewards for some
samples are not observed, and so the logged data consists of a subset of
samples with unknown rewards and a subset of samples with known rewards. This
setting arises in many application domains, including …

arxiv networks neural networks risk

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