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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.

168,742 papers · 148 categories

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3 results for “support-aware”

CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systemscs.IR

CASP selects reliable policies for two-stage recommender systems by considering both value and support.

problem The selection of a generator in two-stage recommender systems affects both the policy value and the data support used to estimate it.

method CASP combines doubly robust value estimation with a support-burden penalty.

result CASP selects lower-burden policies when estimated value and support credibility are in tension.

recommender systems two-stage generator selection support-aware offline selection

2026-04-24abs ↗pdf ↗

Support-aware offline policy selection for advertising marketplacesstat.ML

Develops a support-aware framework for reserve-policy selection in advertising markets.

problem Log-based reserve-price evaluation risks weak support and subgroup harm.

method Support-aware offline decision framework converting logged evidence into certified policies.

result Preserves the best gate-passing policy while eliminating only policies with certified regret.

reserve pricing advertising markets offline evaluation support awareness policy selection

2026-05-20abs ↗pdf ↗

Locally Private Online Quantile Regression: Estimation and Inferencestat.ML

Locally private online quantile regression method addresses privacy constraints.

problem Estimating and inferring quantile regression under local differential privacy constraints.

method Developed a finite-alphabet channel where users compute local contributions, apply randomized response, and send reports. A public decoder corrects distortion and reconstructs inputs for averaging.

result Established local privacy, decoder unbiasedness, consistency, asymptotic normality, and inference for scalar contrasts.

quantile regression local differential privacy online learning privacy-preserving stochastic quantization

2026-07-06abs ↗pdf ↗