Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy

By Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuanshuo Zhou, Jian Peng. 2018

21 citations recorded by the source corpus.

When learning from a batch of logged bandit feedback, the discrepancy between the policy to be learned and the off-policy training data imposes statistical and computational challenges. Unlike classical supervised learning and online learning settings, in batch contextual bandit learning, one only has access to a collection of logged feedback from the actions taken by a historical policy, and expect to learn a policy that takes good actions in possibly unseen contexts. Such a batch learning setting is ubiquitous in online and interactive systems, such as ad platforms and recommendation systems. Existing approaches based on inverse propensity weights, such as Inverse Propensity Scoring (IPS) and Policy Optimizer for Exponential Models (POEM), enjoy unbiasedness but often suffer from large mean squared error. In this work, we introduce a new approach named Maximum Likelihood Inverse Propensity Scoring (MLIPS) for batch learning from logged bandit feedback. Instead of using the given historical policy as the proposal in inverse propensity weights, we estimate a maximum likelihood surrogate policy based on the logged action-context pairs, and then use this surrogate policy as the proposal. We prove that MLIPS is asymptotically unbiased, and moreover, has a smaller nonasymptotic mean squared error than IPS. Such an error reduction phenomenon is somewhat surprising as the estimated surrogate policy is less accurate than the given historical policy. Results on multi-label classification problems and a large- scale ad placement dataset demonstrate the empirical effectiveness of MLIPS. Furthermore, the proposed surrogate policy technique is complementary to existing error reduction techniques, and when combined, is able to consistently boost the performance of several widely used approaches.

Subfields: Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Benchmarks for Deep Off-Policy Evaluation (2021, methodology): “Since the behavior policy is not known explicitly, we learn an estimate of it via a max-likelihood objective over the dataset D , as advocated by Xie et al. (2018); Hanna et al. (2019).”