Transfer Learning Reading Group
Paper for Next Meeting (4 June 2008)
- Transfer in Reinforcement Learning via Markov Logic Networks. L. Torrey, J. Shavlik, S. Natarajan, P. Kuppili & T. Walker. AAAI-08 TL in Complex Tasks Workshop. [PDF]
Candidate Papers
- Convex Point Estimation using Undirected Bayesian Transfer Hierarchies. G. Elidan, B. Packer, G. Heitz, and D. Koller. UAI 08 [PDF]
- Deep Transfer via Second Order Markov Logic. Jesse Davis and Pedro Domingos. AAAI-08 TL in Complex Tasks Workshop. [PDF]
- Revising First-Order Logic Theories from Examples Through Stochastic Local Search. Aline Paes, Gerson Zaverucha and Vitor Santos Costa. ECML-07
http://www.springerlink.com/content/8pp344k41g7735k7/fulltext.pdf
- Effective Control Knowledge Transfer Through Learning Skill and
Representation Hierarchies. Asadi and Huber. IJCAI-07. http://www.ijcai.org/papers07/Papers/IJCAI07-331.pdf
- Transfer Learning in Reinforcement Learning Problems Through
Partial Policy Recycling. Jan Ramon, Kurt Driessens, and Tom
Croonenborghs. ECML-07.Here
- Learning Multiple Related Tasks Using Latent Independent Component Analysis. J. Zhang, Z. Ghahramani, and Y. Yang.
http://nyc.lti.cs.cmu.edu/yiming/Publications/zgy-nips05.pdf NIPS-05
- Semi-Supervised Multitask Learning: Liu, Liao, Carin
- Multi-Task Learning via Conic Programming: Kato, Kashima, Sugiyama, Asai
- A Spectral Regularization Framework for Multi-Task Structure Learning:
Argyriou, Micchelli, Pontil, Ying
- Bayesian Co-Traning: Yu, Krishnapuram, Rosales, Steck, Rao
- Multi-task Gaussian Process Prediction: Williams, Chai, Bonilla
Past readings (Some papers discussed in the
RL reading group are also related to transfer)
- Approximate Policy Iteration with a Policy Language Bias: Solving Relational Markov Decision Processes. Alan Fern, Sungwook Yoon, and Robert Givan. [PDF]
- Learning Relational Options for Inductive Transfer in Relational
Reinforcement Learning. Tom Croonenborghs, Kurt Driessens, and Maurice
Bruynooghe. ILP-07. Here
- Learning from Relevant Tasks Only. Samuel Kaski, Jaakko Peltonen
http://www.springerlink.com/content/v71r033t631g3401/fulltext.pdf
- 3/19/08: Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts. J. Zico Kolter, Marcus A. Maloof
http://www.jmlr.org/papers/volume8/kolter07a/kolter07a.pdf
- 2/20/08: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. John Blitzer, Mark Dredze, and Fernando Pereira.
http://www.cis.upenn.edu/~mdredze/publications/sentiment_acl07.pdf
- 2/6/08: Internal paper (draft of an ICML-08 submission)
- 1/23/08: Learning Bounds for Domain Adaptation. J. Blitzer, K. Crammer, A. Kulesza. F. Pereira, and J. Wortman.
http://www.cis.upenn.edu/~blitzer/papers/adaptation_weighting.pdf
- 12/14/07: Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical
Evaluations.
M. M. Mahmud and Sylvian Ray. http://books.nips.cc/papers/files/nips20/NIPS2007_0327.pdf
- 11/30/07: Boosting for Transfer Learning. Wenyuan Dai,
Quang Yang, Gui-Rong Xue, and Yong
Yu. http://imls.engr.oregonstate.edu/www/htdocs/proceedings/icml2007/papers/72.pdf
- 11/16/07:Learning a meta-level prior for feature relevance from
multiple related tasks. Su-In Lee, Vassil Chatalbashev, David
Vickrey, and Daphne Koller. http://ai.stanford.edu/~vasco/pubs/metaprior.pdf
- 10/25/07: Probabilistic Planning in the Graphplan
Framework. Avrim L. Blum and John C. Langford. http://www.cs.cmu.edu/~jcl/papers/planning/ecp.ps
- 10/12/07: Relational Macros for Transfer in Reinforcement
Learning. Lisa Torrey, Jude Shavlik, Trevor Walker, and Richard
Maclin. ILP-07.
- 9/20/07: Learning Probabilistic Relational Dynamics for
Multiple Tasks. Ashwin Deshpande, Brian Milch, Luke Zettlemoyer
and Leslie Kaelbling. UAI-07. http://people.csail.mit.edu/lsz/papers/dmzk-uai07.pdf
- Transfer of Experience
between Reinforcement Learning Environments with Progressive Difficulty:
Madden & Howley (2004)
- Constructing Informative
Priors using Transfer Learning. Rajat Raina, Andrew Ng,
Daphne Koller. ICML-06.
- Asadi M, Huber M (2005), Accelerating Action Dependent Hierarchical
Reinforcement Learning Through Autonomous Subgoal Discovery, In
ICML 2005 Workshop on Rich Representations for Reinforcement Learning,
Bonn, Germany.
- Asadi M, Huber M (2004), State
Space Reduction For Hierarchical Reinforcement Learning, In
Proceedings of the 17th International FLAIRS Conference, pp. 509 - 514,
Miami Beach, FL.
- G. Casella.An Introduction to Empirical
Bayes Data Analysis, American Statistician, 39(2): pp. 83-87, 1985.
- Alexandru Niculescu-Mizil, Rich Caruana: Learning the Structure
of Related Tasks. In Inductive Transfer NIPS Workshop.
- Neville Mehta, Sriraam Natarajan, Prasad Tadepalli and Alan Fern.
Transfer in Variable Reward
Hierarchical Reinforcement Learning. Inductive Transfer NIPS
workshop 2005.
- Robert
E. Wray, John E. Laird, Andrew Nuxoll, Devvan
Stokes,
Alex Kerfoot, Synthetic
Adversaries for Urban Combat Training, AI Magazine,
26(3):82-92, 2005.
- Ontology Matching: A Machine
Learning Approach, A. Doan, J.
Madhavan, P. Domingos, and A. Halevy. Handbook on Ontologies in
Information Systems, S.
Staab and R. Studer (eds.), Springer-Velag, 2004. Invited paper. Pages
397-416.
- O. Ilghami, H. Muñoz-Avila, D. S. Nau, and
D. W. Aha. Learning approximate
preconditions for methods in hierarchical plans. Proceedings of the
International Conference on Machine Learning (ICML), Aug. 2005.
- O. Ilghami, D. S. Nau, H. Muñoz-Avila,
and D. W. Aha. Learning preconditions
for planning from plan traces and HTN structure. Computational Intelligence 21(4):388–413, Nov.
2005.
- Matthew Richardson,
Pedro Domingos (2006). Markov logic
networks.
Machine Learning 62(1-2): 107-136.
- Stanley Kok,
Pedro Domingos (2005). Learning the
structure of Markov
logic networks.
ICML 2005: 441-448.
- Langley, P. (in press). Cognitive
architectures and general intelligent systems. AI Magazine.
- Langley, P., Choi, D., & Rogers, S. (2005). Interleaving learning, problem
solving, and execution in the Icarus architecture (Technical Report).
Computational Learning Laboratory, CSLI, Stanford University, CA.
Suggested readings from people on the team (and not read yet)
- Ken Forbus
- Larry Holder
- Interfacing the D'Artagnan
Cognitive Architecture to the Urban Terror First-Person Shooter Game,
by Bharat Kondeti, Maheswar Nallacharu, Michael Youngblood, &
Lawrence Holder. In IJCAI 2005 Workshop on Reasoning,
Representation, and Learning in Computer Games.
- John Laird
- Lehman, J. F., Laird, J. E., Rosenbloom, P., E. (2005) A Gentle Introduction to Soar:
2005 Update. Unpublished. An update to an earlier paper (published
in 1996).
- Nason,
S. and Laird, J. E.,Soar-RL,
Integrating Reinforcement Learning with Soar, Cognitive Systems
Research,
6 (1), 2005, pp. 51-59.
- Ray Mooney
- Michael Pazzani
- Peter Stone
- David Stracuzzi
- Michael Witbrock
Current Group Members
- Tuyen Huynh
- Lilyana Mihalkova
- Raymond Mooney
- Peter Stone
- Matthew Taylor
Related resources