MLRG/summer08

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1 Machine Learning Reading Group

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Machine Learning Reading Group

Informal reading group at Utah on machine learning topics (led by Hal). Papers, discussion, etc. will be posted here.

Time: Wed 3:40-5:00pm (see schedule for each week)

Topic: Multitask learning, transfer learning and domain adaptation

Location: Graphics Annex

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Schedule

Date	Papers	Presenter
Historical Interest
W 07 May	Rich Caruana (1997). Multitask learning. Machine Learning, 28, 41-75. Sebastian Thrun (1996). Is learning the nth thing any easier than learning the first? In Advances in NIPS, 640-646.	Hal
The Bayesian Way
W 14 May	Rajat Raina, Andrew Y. Ng and Daphne Koller (2006). Constructing informative priors using transfer learning. ICML.
W 21 May	Kai Yu, Volker Tresp and Anton Schwaighofer (2005). Learning Gaussian Processes from Multiple Tasks. ICML.	Amit
W 28 May	Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram (2007). Multi-task learning for classification with dirichlet process priors. JMLR.	Nathan
The Kernel Way
W 04 Jun	Theodoros Evgeniou, Charles Micchelli, and Massimiliano Pontil (2005). Learning multiple tasks with kernel methods. J. Machine Learning Research, 6: 615--637.	Arvind
Learning Theory and Generalization Bounds
W 11 Jun	Ben-David, S. and Schuller, R. (2003). Exploiting task relatedness for multiple task learning. In Proc COLT, pages 567-580.	Piyush
W 25 Jun	Abernethy, Bartlett and Rakhlin (2007). Multitask Learning with Expert Advice. Technical Report (UC Berkeley).	Arvind
W 2 Jul	M. M. Mahmud, Sylvian Ray (2007). Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations. NIPS.
Domain Adaptation
W 16 Jul	Rie Kubota Ando and Tong Zhang (2005). A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data. Journal of Machine Learning Research, Vol 6:1817-1853, 2005.
W 23 Jul	John Blitzer, Ryan McDonald, and Fernando Pereira (2006). Domain Adaptation with Structural Correspondence Learning. Empirical Methods in Natural Language Processing - EMNLP 2006.	Nathan
W 30 Jul	Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira (2006). Analysis of Representations for Domain Adaptation. NIPS John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jenn Wortman (2007). Learning Bounds for Domain Adaptation. NIPS.	Piyush
W 06 Aug	Sugiyama, M., Nakajima, S., Kashima, H., von Bünau, P. & Kawanabe, M. (2007). Direct importance estimation with model selection and its application to covariate shift adaptation. NIPS.	Chang
W 13 Aug	Steffen Bickel, Michael Bruckner and Tobias Scheffer (2007). Discriminative Learning for Differing Training and Test Distributions. ICML.
W 20 Aug	Jiayuan Huang, Alex Smola, Arthur Gretton, Karsten Borgwardt and Bernhard Scholkopf (2007). Correcting Sample Selection Bias by Unlabeled data. NIPS.