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Found 11 papers, 9 papers with code
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space.
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
The PseudoInverse (PI) estimator has been introduced to mitigate the variance issue by assuming linearity in the reward function, but this can result in significant bias as this assumption is hard-to-verify from observed data and is often substantially violated.
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Existing evaluation metrics for OPE estimators primarily focus on the "accuracy" of OPE or that of downstream policy selection, neglecting risk-return tradeoff in the subsequent online policy deployment.
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation
This paper introduces SCOPE-RL, a comprehensive open-source Python software designed for offline reinforcement learning (offline RL), off-policy evaluation (OPE), and selection (OPS).
Off-Policy Evaluation of Ranking Policies under Diverse User Behavior
We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior.
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Although many estimators have been developed, there is no single estimator that dominates the others, because the estimators' accuracy can vary greatly depending on a given OPE task such as the evaluation policy, number of actions, and noise level.
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions.
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation
In this position paper, we explore the potential of using simulation to accelerate practical research of offline RL and OPE, particularly in RecSys and RTB.
Evaluating the Robustness of Off-Policy Evaluation
Unfortunately, identifying a reliable estimator from results reported in research papers is often difficult because the current experimental procedure evaluates and compares the estimators' performance on a narrow set of hyperparameters and evaluation policies.
Constrained Generalized Additive 2 Model with Consideration of High-Order Interactions
The second is the introduction of a higher-order term: given that GA2M considers only second-order interactions, we aim to balance interpretability and prediction accuracy by introducing a higher-order term that can capture higher-order interactions.
