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This paper presents a co-regularization based approach to semi-supervised domain adaptation. Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further enable the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement and can be applied as a pre-processing step to any supervised learner. Our theoretical analysis (in terms of Rademacher complexity) of EA and EA++ show that the hypothesis class of EA++ has lower complexity (compared to EA) and hence results in tighter generalization bounds. Experimental results on sentiment analysis tasks reinforce our theoretical findings and demonstrate the efficacy of the proposed method when compared to EA as well as a few other baseline approaches.
Author Information
Hal Daumé III (University of Maryland - College Park)
Abhishek Kumar (Google Brain)
Avishek Saha (Yahoo Labs)
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2021 : Poster: The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
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2021 : The Many Roles that Causal Reasoning Plays in Reasoning about Fairness in Machine Learning »
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2018 Workshop: Wordplay: Reinforcement and Language Learning in Text-based Games »
Adam Trischler · Angeliki Lazaridou · Yonatan Bisk · Wendy Tay · Nate Kushman · Marc-Alexandre Côté · Alessandro Sordoni · Daniel Ricks · Tom Zahavy · Hal Daumé III -
2016 Poster: A Credit Assignment Compiler for Joint Prediction »
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2014 Workshop: Representation and Learning Methods for Complex Outputs »
Richard Zemel · Dale Schuurmans · Kilian Q Weinberger · Yuhong Guo · Jia Deng · Francesco Dinuzzo · Hal Daumé III · Honglak Lee · Noah A Smith · Richard Sutton · Jiaqian YU · Vitaly Kuznetsov · Luke Vilnis · Hanchen Xiong · Calvin Murdock · Thomas Unterthiner · Jean-Francis Roy · Martin Renqiang Min · Hichem SAHBI · Fabio Massimo Zanzotto -
2012 Poster: Imitation Learning by Coaching »
He He · Hal Daumé III · Jason Eisner -
2012 Poster: Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression »
Piyush Rai · Abhishek Kumar · Hal Daumé III -
2012 Poster: Learned Prioritization for Trading Off Accuracy and Speed »
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2011 Poster: Message-Passing for Approximate MAP Inference with Latent Variables »
Jiarong Jiang · Piyush Rai · Hal Daumé III -
2011 Poster: Co-regularized Multi-view Spectral Clustering »
Abhishek Kumar · Piyush Rai · Hal Daumé III -
2010 Poster: Learning Multiple Tasks using Manifold Regularization »
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2009 Poster: Multi-Label Prediction via Sparse Infinite CCA »
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2008 Poster: Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression »
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2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
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2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
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