Meta-learners
Meta-Learners is a class of algorithmic frameworks in causal inference that decomposes the estimation of heterogeneous treatment effects into separate, reweighted machine learning tasks to isolate treatment-specific response patterns from observational data.
141
Publications
8.2K
Citations
538
Authors
264
Institutions
Publications per year
2017–2026
110
141
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Zhenguo Li, Fengwei Zhou, Fei Chen et al. · arXiv (Cornell University) · 2017 · 845 citations · Full text
Reconciling schemas of disparate data sources
AnHai Doan, Pedro Domingos, Alon Halevy · 2001 · 733 citations
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264
Tsinghua University
Beijing, China
4 h-index
6 publications
346 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
14 publications
492 citations
Universidade de São Paulo
São Paulo, Brazil
3 h-index
5 publications
203 citations
University of Geneva
Geneva, Switzerland
3 h-index
5 publications
281 citations
University of California, Berkeley
Berkeley, United States
3 h-index
7 publications
685 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
5 publications
288 citations

Zhejiang University
Hangzhou, China
3 h-index
13 publications
807 citations
National University of Singapore
Singapore, Singapore
3 h-index
10 publications
630 citations
1–8 of 264
Repository · 16 publications · 1.4K citations
13
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4 publications · 632 citations
4
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5 publications · 440 citations
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MSSP · 3 publications · 415 citations
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Conference · 2 publications · 50 citations
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