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ARXIV:2603.09436 · OFF-POLICY EVALUATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.09436OFF-POLICY EVALUATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias.
Opportunity summary
Pain A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias. This historical data typically does not faithfully represent action distribution of the new policy accurately.
We study off-policy evaluation in the setting of contextual bandits, where we aim to evaluate a new policy using historical data that consists of contexts, actions and received rewards. This historical data typically does…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our NW approach achieves low bias like IPW but typically exhibits significantly lower variance.
Off-Policy Evaluation moved forward this cycle; last verified April 2026. Public score 2.0/10.
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A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias.
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10.48550/arXiv.2603.09436A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias.
Abstract
We study off-policy evaluation in the setting of contextual bandits, where we aim to evaluate a new policy using historical data that consists of contexts, actions and received rewards. This historical data typically does not faithfully represent action distribution of the new policy accurately. A common approach, inverse probability weighting (IPW), adjusts for these discrepancies in action distributions. However, this method often suffers from high variance due to the probability being in the denominator. The doubly robust (DR) estimator reduces variance through modeling reward but does not directly address variance from IPW. In this work, we address the limitation of IPW by proposing a Nonparametric Weighting (NW) approach that constructs weights using a nonparametric model. Our NW approach achieves low bias like IPW but typically exhibits significantly lower variance. To further reduce variance, we incorporate reward predictions -- similar to the DR technique -- resulting in the Model-assisted Nonparametric Weighting (MNW) approach. The MNW approach yields accurate value estimates by explicitly modeling and mitigating bias from reward modeling, without aiming to guarantee the standard doubly robust property. Extensive empirical comparisons show that our approaches consistently outperform existing techniques, achieving lower variance in value estimation while maintaining low bias.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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PROBLEM
A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias. This historical data typically does not faithfully represent action distribution of the new policy accurately.
METHOD
We study off-policy evaluation in the setting of contextual bandits, where we aim to evaluate a new policy using historical data that consists of contexts, actions and received rewards. This historical data typically does not faithfully represent action distribution of the new p...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our NW approach achieves low bias like IPW but typically exhibits significantly lower variance.
WHY NOW
Off-Policy Evaluation moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias. This historical data typically does not faithfully represent action distribution of the new policy accurately.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
We study off-policy evaluation in the setting of contextual bandits, where we aim to evaluate a new policy using historical data that consists of contexts, actions and received rewards. This historical data typically does not faithfully represent action distribution of the new policy accurately.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our NW approach achieves low bias like IPW but typically exhibits significantly lower variance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Off-Policy Evaluation moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A novel nonparametric estimator for off-policy evaluation in contextual bandits that reduces variance while maintaining low bias.
Segment
Off-Policy Evaluation
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Commercial read
2.0/10 public viability
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proof status
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