Feb. 5, 2024, 6:43 a.m. | Marcos Matabuena Juan C. Vidal Oscar Hernan Madrid Padilla Jukka-Pekka Onnela

cs.LG updates on arXiv.org arxiv.org

In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique. This method focuses on accurately estimating the conditional mean and variance of random response variables, thereby effectively characterizing conditional distributions across diverse scenarios.Our approach incorporates a robust uncertainty quantification mechanism, leveraging our prior estimation work on conditional mean and variance. The employment of kNN ensures scalable computational efficiency in predicting intervals and statistical accuracy …

adaptability algorithm automated cs.lg knn mean non-parametric novel paper parametric quantification random regression scalability stat.co stat.me stat.ml uncertainty variables variance

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Data Analyst (Digital Business Analyst)

@ Activate Interactive Pte Ltd | Singapore, Central Singapore, Singapore