Feb. 13, 2024, 5:46 a.m. | Andre Wibisono Yihong Wu Kaylee Yingxi Yang

stat.ML updates on arXiv.org arxiv.org

We study the problem of estimating the score function of an unknown probability distribution $\rho^*$ from $n$ independent and identically distributed observations in $d$ dimensions. Assuming that $\rho^*$ is subgaussian and has a Lipschitz-continuous score function $s^*$, we establish the optimal rate of $\tilde \Theta(n^{-\frac{2}{d+4}})$ for this estimation problem under the loss function $\|\hat s - s^*\|^2_{L^2(\rho^*)}$ that is commonly used in the score matching literature, highlighting the curse of dimensionality where sample complexity for accurate score estimation grows exponentially …

bayes continuous dimensions distributed distribution function independent loss math.st probability rate stat.ml stat.th study via

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Scientist AI / ML - Associate 2 -Bangalore

@ PwC | Bengaluru (SDC) - Bagmane Tech Park

Staff ML Engineer - Machine Learning

@ Visa | Bengaluru, India

Senior Data Scientist

@ IQVIA | Dublin, Ireland

Data Analyst ETL Expert

@ Bosch Group | Bengaluru, India