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Multi-Source Domain Adaptation with Mixture of Experts

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arxiv 1809.02256 v2 pith:COUGGLPV submitted 2018-09-07 cs.CL

Multi-Source Domain Adaptation with Mixture of Experts

Jiang Guo , Darsh J Shah , Regina Barzilay This is my paper
classification cs.CL
keywords adaptationapproachdomaindomainsmetricmultiplerelationshipunsupervised
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using meta-training. Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer.

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Cited by 3 Pith papers

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    TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datase...

  3. Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls

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    CS-ARM-BN uses negative control samples to stabilize Batch Normalization statistics in a meta-learning framework, achieving robust MoA classification on new experimental batches under label shift and small sample sizes.