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Found 6 papers, 3 papers with code
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning
We design and implement an adaptive experiment (a ``contextual bandit'') to learn a targeted treatment assignment policy, where the goal is to use a participant's survey responses to determine which charity to expose them to in a donation solicitation.
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
In particular, when the pattern of treatment assignment in the collected data looks little like the pattern generated by the policy to be evaluated, the importance weights used in DR estimators explode, leading to excessive variance.
Adapting to Misspecification in Contextual Bandits with Offline Regression Oracles
Computationally efficient contextual bandits are often based on estimating a predictive model of rewards given contexts and arms using past data.
Tractable contextual bandits beyond realizability
When realizability does not hold, our algorithm ensures the same guarantees on regret achieved by realizability-based algorithms under realizability, up to an additive term that accounts for the misspecification error.
Confidence Intervals for Policy Evaluation in Adaptive Experiments
In this context, typical estimators that use inverse propensity weighting to eliminate sampling bias can be problematic: their distributions become skewed and heavy-tailed as the propensity scores decay to zero.
Sufficient Representations for Categorical Variables
Many learning algorithms require categorical data to be transformed into real vectors before it can be used as input.
