Two new research papers on arXiv introduce advanced quantile regression techniques. The first paper details pairwise quantile regression, establishing theoretical guarantees and demonstrating its application in facial recognition error analysis. The second paper presents a locally private online quantile regression method, enabling estimation and inference under strict privacy protocols, with simulations showing its effectiveness and a New York City taxi-trip illustration. AI
IMPACT Introduces novel statistical methods for analyzing complex data distributions and ensuring privacy in machine learning applications.
RANK_REASON Two academic papers published on arXiv detailing new statistical learning methodologies.
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- New York City
- Polyak--Ruppert
- ScienceCast
- Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms
- U-Processes and Preference Learning
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