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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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1 result for “De-Amplification”
Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecificationstat.ML

New method improves experimental design under model misspecification.

problem Improving experimental design under time and budget constraints with model misspecification.
method Developed a new acquisition function (R-IDeA) that targets representativeness, informativeness, and de-amplification.
result The new method outperforms methods focusing on representativeness or informativeness alone.
Bayesian Optimal Experimental DesignModel MisspecificationCovariate ShiftDe-AmplificationExperimental Design
2025-06-09abs ↗pdf ↗