Doubly robust distributionally robust off-policy evaluation and learning
Off-policy evaluation and learning (OPE/L) use offline observational data to make better
decisions, which is crucial in applications where online experimentation is limited. However …
decisions, which is crucial in applications where online experimentation is limited. However …
Double reinforcement learning for efficient off-policy evaluation in markov decision processes
Off-policy evaluation (OPE) in reinforcement learning allows one to evaluate novel decision
policies without needing to conduct exploration, which is often costly or otherwise infeasible …
policies without needing to conduct exploration, which is often costly or otherwise infeasible …
Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings
where experimentation is limited, such as healthcare. But, in these very same settings …
where experimentation is limited, such as healthcare. But, in these very same settings …
Efficiently breaking the curse of horizon in off-policy evaluation with double reinforcement learning
Off-policy evaluation (OPE) in reinforcement learning is notoriously difficult in long-and
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …
infinite-horizon settings due to diminishing overlap between behavior and target policies. In …
Intrinsically efficient, stable, and bounded off-policy evaluation for reinforcement learning
Off-policy evaluation (OPE) in both contextual bandits and reinforcement learning allows
one to evaluate novel decision policies without needing to conduct exploration, which is …
one to evaluate novel decision policies without needing to conduct exploration, which is …
Off-policy evaluation via adaptive weighting with data from contextual bandits
It has become increasingly common for data to be collected adaptively, for example using
contextual bandits. Historical data of this type can be used to evaluate other treatment …
contextual bandits. Historical data of this type can be used to evaluate other treatment …
State-action similarity-based representations for off-policy evaluation
In reinforcement learning, off-policy evaluation (OPE) is the problem of estimating the
expected return of an evaluation policy given a fixed dataset that was collected by running …
expected return of an evaluation policy given a fixed dataset that was collected by running …
Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using
data generated by a different policy. Because of its huge potential impact in practice, there …
data generated by a different policy. Because of its huge potential impact in practice, there …