Transfer Learning in Nonparametric Regression with Deep ReLU Networks
This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviations in additive form, the proposed method employs a two-stage offset learning procedure: the first stage pools data from all groups to estimate an overall mean function, and the second stage estimates offsets for each group, yielding final group-level estimators through additive combination. Upper bounds on the $\mathcal L_2$ error are established for the proposed framework, cov
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- PossiblePossibly related (embedding) · 48%Transfer learning with deployment-covariate recalibration for survival prediction under covariate shift →
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Transfer Learning in Nonparametric Regression with Deep ReLU” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Junpeng Ren →
“Transfer Learning in Nonparametric Regression with Deep ReLU Networks”
- LinkedLinked via arxiv author · 85%Carlos Misael Madrid Padilla →
“Transfer Learning in Nonparametric Regression with Deep ReLU Networks”
- LinkedLinked via arxiv author · 85%Yanzhen Chen →
“Transfer Learning in Nonparametric Regression with Deep ReLU Networks”
- LinkedLinked via arxiv author · 85%Oscar Hernan Madrid Padilla →
“Transfer Learning in Nonparametric Regression with Deep ReLU Networks”
- FuzzySimilar title/name (fuzzy) · 66%kmario23/deep-learning-drizzle →
“Fuzzy title match (0.78): “Transfer Learning in Nonparametric Regression with Deep ReLU” ≈ “kmario23/deep-learning-drizzle””