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Found 8 papers, 0 papers with code
Selective Uncertainty Propagation in Offline RL
We consider the finite-horizon offline reinforcement learning (RL) setting, and are motivated by the challenge of learning the policy at any step h in dynamic programming (DP) algorithms.
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.
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles
Contextual bandit algorithms often estimate reward models to inform decision-making.
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective
Our algorithm is based on a novel misspecification test, and our analysis demonstrates the benefits of using model selection for reward estimation.
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.
Groupwise Maximin Fair Allocation of Indivisible Goods
We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations.
