A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization

Variance regularized counterfactual risk minimization (VRCRM) has been proposed as an alternative off-policy learning (OPL) method. VRCRM method uses a lower-bound on the f-divergence between the logging policy and the target policy as regularization during learning and was shown to improve performance over existing OPL alternatives on multi-label classification tasks. In this work, we revisit the original experimental setting of VRCRM and propose to minimize the f-divergence directly, instead of optimizing for the lower bound using a f-GAN approach. Surprisingly, we were unable to reproduce the results reported in the original setting. In response, we propose a novel simpler alternative to f-divergence optimization by minimizing a direct approximation of f-divergence directly, instead of a f-GAN based lower bound. Experiments showed that minimizing the divergence using f-GANs did not work as expected, whereas our proposed novel simpler alternative works better empirically.