Communications of the ACM
Statistical methods for speech recognition
Statistical methods for speech recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised and unsupervised PCFG adaptation to novel domains
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Adaptive language modeling using minimum discriminant estimation
HLT '91 Proceedings of the workshop on Speech and Natural Language
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Multiple source adaptation and the Rényi divergence
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition). The learning theory community has only recently started to analyze domain adaptation problems. In the talk, I will overview some recent theoretical models and results regarding domain adaptation. This talk is based on joint works with Mehryar Mohri and Afshin Rostamizadeh.