PulseAugur
EN
LIVE 14:21:24
ENTITY Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression

Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression

PulseAugur coverage of Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression — every cluster mentioning Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression across labs, papers, and developer communities, ranked by signal.

Show in brief
Total · 30d
1
1 over 90d
Releases · 30d
0
0 over 90d
Papers · 30d
1
1 over 90d
TIER MIX · 90D
TOPICS
SENTIMENT · 30D

1 day(s) with sentiment data

RECENT · PAGE 1/1 · 1 TOTAL
  1. RESEARCH · CL_65243 ·

    New method improves semi-supervised learning with noisy data

    Researchers have developed a new semi-supervised regression method designed for scenarios with abundant noisy proxy covariates and scarce task-specific labels. The proposed two-stage estimator learns kernel eigenfeature…