Summary of A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization, by Hua Chang Bakker et al.
A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization
by Hua Chang Bakker, Shashank Gupta, Harrie Oosterhuis
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Variance regularized counterfactual risk minimization (VRCRM) has been proposed as an alternative off-policy learning method. The original VRCRM uses a lower-bound on the f-divergence between the logging policy and the target policy as regularization during learning, which was shown to improve performance over existing OPL alternatives on multi-label classification tasks. This paper revisits the original experimental setting of VRCRM and proposes minimizing the f-divergence directly instead of optimizing for the lower bound using a f-GAN approach. The authors were unable to reproduce the results reported in the original setting, leading them to propose a novel simpler alternative to f-divergence optimization by minimizing a direct approximation of f-divergence directly. Experiments show that minimizing the divergence using f-GANs did not work as expected, whereas the proposed alternative works better empirically. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Variance regularized counterfactual risk minimization is a new way for machines to learn from experiences without following rules. Some researchers thought this method would work well on certain tasks, but when they tried it, they didn’t get the same results as others. Instead of using a tricky way to calculate something called f-divergence, the authors propose a simpler approach that seems to work better. |
Keywords
» Artificial intelligence » Classification » Gan » Optimization » Regularization
