Learning to act in noisy contexts using deep proxy learning

MBZUAI · Notable

Summary

Researchers are exploring methods for evaluating the outcome of actions using off-policy observations where the context is noisy or anonymized. They employ proxy causal learning, using two noisy views of the context to recover the average causal effect of an action without explicitly modeling the hidden context. The implementation uses learned neural net representations for both action and context, and demonstrates outperformance compared to an autoencoder-based alternative. Why it matters: This research addresses a key challenge in applying AI in real-world scenarios where data privacy or bandwidth limitations necessitate working with noisy or anonymized data.

Keywords

proxy learning · causal inference · noisy data · off-policy · anonymization

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MBZUAI · Invalid Date

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MBZUAI · Invalid Date

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