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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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2 results for “CUPED”

Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratificationcs.LG

We reduce variance in monetization metrics for ranking experiments.

problem Heavy-tailed monetization metrics lead to unreliable conclusions in A/B experiments.

method Post-stratification combined with CUPED.

result Significant reduction in variance and improved decision stability.

variance reduction monetization metrics ranking experiments post-stratification CUPED

2026-06-02abs ↗pdf ↗

Unifying On- and Off-Policy Variance Reduction Methodsstat.ML

This paper unifies two types of statistical methods for estimating treatment effects.

problem Isolating online A/B-tests and off-policy evaluation.

method Establishes formal equivalence between online Difference-in-Means and off-policy Inverse Propensity Scoring methods.

result Standard online methods are mathematically equivalent to off-policy methods with optimal control variates.

statistical methods treatment effects online A/B-tests off-policy evaluation Doubly Robust estimation

2026-03-09abs ↗pdf ↗