Fetching the paper…
Reading the bibliography…
We present a theoretical and algorithmic study of the multiple-source domain adaptation problem in the common scenario where the learner has access only to a limited amount of labeled target data, but where the learner has at disposal a large amount of labeled data from multiple source domains.
Problem complexity and Method Efficiency in Optimization
Arkadii Semenovich Nemirovski and David Berkovich Yudin · 1983
Earlier work this paper cites.
Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains
J-L Gauvain and Chin-Hui Lee · 1994
Earlier work this paper cites.
Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models
Christopher J Leggetter and Philip C Woodland · 1995
Earlier work this paper cites.
Statistical methods for speech recognition
Frederick Jelinek · 1997
Earlier work this paper cites.
Detecting change in data streams
D. Kifer, S. Ben-David, and J. Gehrke · 2004
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
Earlier work this paper cites.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
John Blitzer, Mark Dredze, and Fernando Pereira · 2007
Earlier work this paper cites.
Frustratingly hard domain adaptation for dependency parsing
Mark Dredze, John Blitzer, Partha Talukdar, Kuzman Ganchev, Joao Graca, and Fernando Pereira · 2007
Earlier work this paper cites.
Instance weighting for domain adaptation in nlp
Jing Jiang and ChengXiang Zhai · 2007
Earlier work this paper cites.
Cross-domain video concept detection using adaptive svms
Jun Yang, Rong Yan, and Alexander G. Hauptmann · 2007
Earlier work this paper cites.
Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman · 2008
Earlier work this paper cites.
Learning from multiple sources
Koby Crammer, Michael J. Kearns, and Jennifer Wortman · 2008
Earlier work this paper cites.
Domain adaptation from multiple sources via auxiliary classifiers
Lixin Duan, Ivor W. Tsang, Dong Xu, and Tat-Seng Chua · 2009
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
Earlier work this paper cites.
MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
Earlier work this paper cites.
Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
Earlier work this paper cites.
Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee, and Clayton Scott · 2011
Earlier work this paper cites.
Domain adaptation in regression
Corinna Cortes and Mehryar Mohri · 2011
Cited alongside, same era.
Solving variational inequalities with stochastic mirror-prox algorithm
Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel · 2011
Cited alongside, same era.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Cited alongside, same era.
A two-stage weighting framework for multi-source domain adaptation
Qian Sun, Rita Chattopadhyay, Sethuraman Panchanathan, and Jieping Ye · 2011
Cited alongside, same era.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
Cited alongside, same era.
Domain adaptation from multiple sources: A domain-dependent regularization approach
Lixin Duan, Dong Xu, and Ivor Wai-Hung Tsang · 2012
Cited alongside, same era.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Later among the works it cites.
Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
Later among the works it cites.
Learning attributes equals multi-source domain generalization
Chuang Gan, Tianbao Yang, and Boqing Gong · 2016
Later among the works it cites.
Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
Later among the works it cites.
Structure-preserved multi-source domain adaptation
Hongfu Liu, Ming Shao, and Yun Fu · 2016
Later among the works it cites.
Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha, and Kristen Grauman · 2012
Cited alongside, same era.
Discovering latent domains for multisource domain adaptation
Judy Hoffman, Brian Kulis, Trevor Darrell, and Kate Saenko · 2012
Cited alongside, same era.
Robust visual domain adaptation with low-rank reconstruction
I-Hong Jhuo, Dong Liu, DT Lee, and Shih-Fu Chang · 2012
Cited alongside, same era.
Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A. Efros, and Antonio Torralba · 2012
Cited alongside, same era.
New analysis and algorithm for learning with drifting distributions
Mehryar Mohri and Andres Muñoz Medina · 2012
Cited alongside, same era.
Efficiency of coordinate descent methods on huge-scale optimization problems
Yu Nesterov · 2012
Cited alongside, same era.
Later among the works it cites.
Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, and Seungil You · 2018
Later among the works it cites.
Algorithms and theory for multiple-source adaptation
Judy Hoffman, Mehryar Mohri, and Ningshan Zhang · 2018
Later among the works it cites.
Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
Later among the works it cites.
Multi-adversarial domain adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang · 2018
Later among the works it cites.
Adversarial multiple source domain adaptation
Han Zhao, Shanghang Zhang, Guanhang Wu, José MF Moura, Joao P Costeira, and Geoffrey J Gordon · 2018
Later among the works it cites.
Robust learning from untrusted sources
Nikola Konstantinov and Christoph Lampert · 2019
Later among the works it cites.
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh · 2019
Later among the works it cites.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Later among the works it cites.
Semi-supervised domain adaptation via minimax entropy
Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Trevor Darrell, and Kate Saenko · 2019
Later among the works it cites.
Few-shot adaptive faster r-cnn
Tao Wang, Xiaopeng Zhang, Li Yuan, and Jiashi Feng · 2019
Later among the works it cites.
Domain aggregation networks for multi-source domain adaptation
Junfeng Wen, Russell Greiner, and Dale Schuurmans · 2019
Later among the works it cites.
Multiple-source adaptation theory and algorithms
Judy Hoffman, Mehryar Mohri, and Ningshan Zhang · 2020
Closest in time.