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I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable
  Models

I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models

20 February 2020
Adarsh Subbaswamy
Suchi Saria
    OOD
ArXiv (abs)PDFHTML

Papers citing "I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models"

5 / 5 papers shown
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
Thomas A. Lasko
Eric V. Strobl
William W Stead
OOD
349
23
0
08 Nov 2023
A Survey on Causal Representation Learning and Future Work for Medical
  Image Analysis
A Survey on Causal Representation Learning and Future Work for Medical Image Analysis
Chang-Tien Lu
OODBDLCMLMedIm
279
0
0
28 Oct 2022
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for
  Pre-training Debiasing
Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training DebiasingInternational Conference on Information and Knowledge Management (CIKM), 2021
Sindhu C. M. Gowda
Shalmali Joshi
Haoran Zhang
Marzyeh Ghassemi
CML
261
10
0
27 Aug 2021
Domain Adaptation as a Problem of Inference on Graphical Models
Domain Adaptation as a Problem of Inference on Graphical ModelsNeural Information Processing Systems (NeurIPS), 2020
Kun Zhang
Biwei Huang
P. Stojanov
Erdun Gao
Qingsong Liu
Clark Glymour
OOD
529
72
0
09 Feb 2020
A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning
  Algorithms
A Unifying Causal Framework for Analyzing Dataset Shift-stable Learning AlgorithmsJournal of Causal Inference (JCI), 2019
Adarsh Subbaswamy
Bryant Chen
Suchi Saria
OOD
456
27
0
27 May 2019
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