Tag: off
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Off-Beat Careers That Are the Future Of Data
Off-Beat Careers That Are the Future Of Data The unconventional career paths you need to explore The post Off-Beat Careers That Are the Future Of Data appeared first on Towards Data Science. Rashi Desai Go to original source
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Convergence of off-policy TD(0) with linear function approximation for reversible Markov chains
Convergence of off-policy TD(0) with linear function approximation for reversible Markov chains arXiv:2510.25514v1 Announce Type: new Abstract: We study the convergence of off-policy TD(0) with linear function approximation when used to approximate the expected discounted reward in a Markov chain. It is well known that the combination of off-policy learning and function approximation can lead…
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DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current Effects arXiv:2505.00961v1 Announce Type: new Abstract: Off-policy evaluation (OPE) and off-policy learning (OPL) for contextual bandit policies leverage historical data to evaluate and optimize a target policy. Most existing OPE/OPL methods–based on importance weighting or imputation–assume common support between the target and logging policies. When this assumption…
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Off-Policy Evaluation for Recommendations with Missing-Not-At-Random Rewards
Off-Policy Evaluation for Recommendations with Missing-Not-At-Random Rewards arXiv:2502.08993v1 Announce Type: new Abstract: Unbiased recommender learning (URL) and off-policy evaluation/learning (OPE/L) techniques are effective in addressing the data bias caused by display position and logging policies, thereby consistently improving the performance of recommendations. However, when both bias exits in the logged data, these estimators may suffer…