Pith. sign in

REVIEW 2 cited by

Cross-Validated Off-Policy Evaluation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.15332 v4 pith:QMO67W62 submitted 2024-05-24 cs.LG

Matej Cief , Branislav Kveton , Michal Kompan This is my paper · ORCID
classification cs.LG
keywords evaluationoff-policycross-validationmethodpopularselectionaddressesalthough
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We study estimator selection and hyper-parameter tuning in off-policy evaluation. Although cross-validation is the most popular method for model selection in supervised learning, off-policy evaluation relies mostly on theory, which provides only limited guidance to practitioners. We show how to use cross-validation for off-policy evaluation. This challenges a popular belief that cross-validation in off-policy evaluation is not feasible. We evaluate our method empirically and show that it addresses a variety of use cases.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits

    cs.LG 2026-07 reject novelty 6.0 of 10

    COPE/COPE-PG, a cross-domain off-policy evaluation and learning method, leverages source-domain data to estimate and optimize target-domain policies even with few-shot data, deterministic logging, and completely new actions.

  2. Off-Policy Evaluation and Learning for the Future under Non-Stationarity

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A new importance-weighted estimator, OPFV, estimates and optimizes future policy value in non-stationary bandit environments by leveraging recurring time features in historical logs.

Pith tools