ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Density Ratio Estimation: A New Versatile Tool for Machine Learning
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Semi-supervised speaker identification under covariate shift
Signal Processing
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
How to Explain Individual Classification Decisions
The Journal of Machine Learning Research
On-line learning: where are we so far?
Ubiquitous knowledge discovery
On-line learning: where are we so far?
Ubiquitous knowledge discovery
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Journal of Biomedical Informatics
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
A unifying view on dataset shift in classification
Pattern Recognition
Cross-Lingual Adaptation Using Structural Correspondence Learning
ACM Transactions on Intelligent Systems and Technology (TIST)
On the usefulness of similarity based projection spaces for transfer learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
On the dataset shift problem in software engineering prediction models
Empirical Software Engineering
Expert Systems with Applications: An International Journal
Robustness of classifiers to changing environments
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Transfer learning with local smoothness regularizer
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Kinship verification through transfer learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Undoing the damage of dataset bias
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Discovering latent domains for multisource domain adaptation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Learning deep belief networks from non-stationary streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A unified classification model based on robust optimization
Neural Computation
Transfer joint embedding for cross-domain named entity recognition
ACM Transactions on Information Systems (TOIS)
FIDOS: A generalized Fisher based feature extraction method for domain shift
Pattern Recognition
Undo the codebook bias by linear transformation for visual applications
Proceedings of the 21st ACM international conference on Multimedia
Cross-domain personality prediction: from video blogs to small group meetings
Proceedings of the 15th ACM on International conference on multimodal interaction
Democracy is good for ranking: towards multi-view rank learning and adaptation in web search
Proceedings of the 7th ACM international conference on Web search and data mining
Quality estimation for machine translation: some lessons learned
Machine Translation
Information Sciences: an International Journal
Information Sciences: an International Journal
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail to recognize spam that differs in form from the spam the automatic filter has been built on.) Despite this, and despite the attention given to the apparently similar problems of semi-supervised learning and active learning, dataset shift has received relatively little attention in the machine learning community until recently. This volume offers an overview of current efforts to deal with dataset and covariate shift. The chapters offer a mathematical and philosophical introduction to the problem, place dataset shift in relationship to transfer learning, transduction, local learning, active learning, and semi-supervised learning, provide theoretical views of dataset and covariate shift (including decision theoretic and Bayesian perspectives), and present algorithms for covariate shift. Contributors: Shai Ben-David, Steffen Bickel, Karsten Borgwardt, Michael Brckner, David Corfield, Amir Globerson, Arthur Gretton, Lars Kai Hansen, Matthias Hein, Jiayuan Huang, Takafumi Kanamori, Klaus-Robert Mller, Sam Roweis, Neil Rubens, Tobias Scheffer, Marcel Schmittfull, Bernhard Schlkopf, Hidetoshi Shimodaira, Alex Smola, Amos Storkey, Masashi Sugiyama, Choon Hui Teo Neural Information Processing series