Unbounded Density Ratio Estimation and Its Application to Covariate Shift Adaptation
2026-04-01T07:23:56Z•2af2072599e062ef5d1ea13f4ce3db1f9ddafdd3fd0d63c14cdbe44e79095545
aircraft-designarxivbayesian-optimizationbregman-divergenceconcept-alignmentcovariate-shiftcross-validationdensity-ratioimportance-weightinginstrumental-variablesinterpretabilityl1-nmfmachine-learningmlr3mbomulti-fidelitynonnegative-matrix-factorizationoaxaca-blinderpenalized-gmmpositive-definite-matricesspatial-predictionstatisticstarget-weighted-cvtransfer-learning
What happened
This record is an arXiv (stat/ML) RSS feed (2026-04-01) aggregating ten new papers in machine learning and statistics. Key contributions include: a novel three-step estimator for unbounded density ratios with guarantees for covariate-shift adaptation; mlr3mbo, a modular Bayesian-optimization toolbox for R; theoretical/variational results for symmetrizing Bregman divergences on positive-definite matrices; multi-fidelity and transfer-learning strategies for constrained Bayesian optimization with aircraft-design applications; a geometric framework for diagnosing and reducing misalignment between人
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_stat_ml
- Record identifier
- 2af2072599e062ef5d1ea13f4ce3db1f9ddafdd3fd0d63c14cdbe44e79095545
- Enrichment time
- 2026-04-01T07:23:56Z
- AI-assisted enrichment
- Yes
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