- Learning and Transferring Roles in Multi-Agent Reinforcement Aaron Wilson and Alan Fern and Soumya Ray and Prasad Tadepalli
- Journal of Machine Learning Research 5 (2004) 13911415 Submitted 3/04; Published 10/04 The Entire Regularization Path for the Support Vector Machine
- Statistical Science 2008, Vol. 23, No. 1, 4547
- Mach Learn (2009) 77: 103123 DOI 10.1007/s10994-009-5119-5
- Hierarchical Topic Models and the Nested Chinese Restaurant Process
- Supervised versus Multiple Instance Learning: An Empirical Comparison
- Journal of Machine Learning Research 9 (2008) 131-156 Submitted 10/05; Revised 7/07; Published 2/08 Evidence Contrary to the Statistical View of Boosting
- Automatic Discovery and Transfer of MAXQ Hierarchies Neville Mehta mehtane@eecs.oregonstate.edu
- Learning Bayesian Network Structure from Correlation-Immune Data
- Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach
- Appears in Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-2003). Skewing: An Efficient Alternative to Lookahead for Decision Tree Induction
- Multiple Instance Regression Soumya Ray sray@cs.wisc.edu
- The Bulletin of Symbolic Logic Volume 14, Number 3, Sept. 2008
- Bayesian Multiple Instance Learning: Automatic Feature Selection and Inductive Transfer
- Toward Optimal Feature Selection Daphne Koller Mehran Sahami
- Optimal Reverse Prediction A Unified Perspective on Supervised, Unsupervised and Semi-supervised Learning
- Tutorial on variational approximation methods Tommi S. Jaakkola
- Hidden Process Models Rebecca A. Hutchinson RAH@CS.CMU.EDU
- Statistical Science 2008, Vol. 23, No. 1, 122
- The maturing architecture of the brain's default network
- The Uniqueness of a Good Optimum for K-Means Marina Meila mmp@stat.washington.edu
- Distributed neural system for general intelligence revealed by lesion mapping
- Max-margin classification of incomplete data Gal Chechik1
- Multiple Non-Redundant Spectral Clustering Views Donglin Niu dniu@ece.neu.edu
- Bandit based Monte-Carlo Planning Levente Kocsis and Csaba Szepesvari
- Automatic Induction of MAXQ Hierarchies Neville Mehta, Mike Wynkoop, Soumya Ray, Prasad Tadepalli, and Tom Dietterich
- LETTER Communicated by Peter Dayan Reinforcement Learning in Continuous Time and Space
- Journal of Arti cial Intelligence Research 13 2000 227-303 Submitted 11 99; published 11 00 Hierarchical Reinforcement Learning with the MAXQ Value
- Utile Coordination: Learning interdependencies among cooperative agents1 Jelle R. Kok
- Efficient Structure Learning of Markov Networks using L1-Regularization
- A Tutorial on Spectral Clustering Ulrike von Luxburg
- Representing Sentence Structure in Hidden Markov Models for Information Extraction
- Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Author(s): Yoav Benjamini and Yosef Hochberg
- Modularity and community structure in networks M. E. J. Newman*
- DOI: 10.1126/science.1165620 , 85 (2009);324Science
- Bayesian Policy Search for Multi-Agent Role Discovery Aaron Wilson and Alan Fern and Prasad Tadepalli
- DOI: 10.1126/science.1194144 , 1358 (2010);329Science
- To Appear in Advances in Neural Information Processing Systems (NIPS) Vol. 20. MIT Press, 2008. Software and data presented in this paper are available online: http://pages.cs.wisc.edu/~bsettles/amil/
- Multiple Instance Learning for Sparse Positive Bags Razvan C. Bunescu razvan@cs.utexas.edu
- Kernel Methods for Missing Variables Alex J. Smola, S.V.N. Vishwanathan
- Journal of Machine Learning Research 7 (2006) 14091436 Submitted 10/05; Revised 3/06; Published 7/06 Fast SDP Relaxations of Graph Cut Clustering,
- Efficient Bandit Algorithms for Online Multiclass Prediction Sham M. Kakade sham@tti-c.org
- Online Planning for Resource Production in Real-Time Strategy Games Hei Chan, Alan Fern, Soumya Ray, Nick Wilson and Chris Ventura
- MaxPlanckInstitut fur biologische Kybernetik Max Planck Institute for Biological Cybernetics
- Incomplete-Data Classification using Logistic Regression David Williams dpw@ee.duke.edu
- The new engl and jour nal of medicine n engl j med 360;17 nejm.org april 23, 2009 1759
- Coordinated Reinforcement Learning Carlos Guestrin GUESTRIN@CS.STANFORD.EDU
- Empirical Bayes for Learning to Learn Tom Heskes tom@mbfys.kun.nl
- An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction
- Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text
- Blockwise Coordinate Descent Procedures for the Multi-task Lasso, with Applications to Neural Semantic Basis Discovery
- Learning from Incomplete Data with Infinite Imputations Uwe Dick dick@mpi-sb.mpg.de
- Ensemble Methods in Machine Learning Thomas G. Dietterich
- Journal of Japanese Society for ArtificialIntelligence,5 67800 57 September, 1999.
- EECS 440: Machine Learning Programming Assignment 2 Assigned Monday September 28, due Wednesday October 21.
- EECS 440 Presentation and Project Report Guide The final project grade will be based on two components: a written report and a presentation in
- EECS 440: Machine Learning (Fall 2009) Programming Assignment 0 Assigned Wednesday August 26. Do not turn in this assignment. This assignment is not
- Data Clustering: A Review Michigan State University
- Data Mining and Knowledge Discovery, 2, 121167 (1998) c 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Assigned Monday January 26, due Wednesday February 18. Turn in your code and writeup using the Digital Dropbox facility of blackboard. Please comment your code extensively so we
- EECS 491 Project Report Guide EECS 491 students are required to submit a detailed report documenting their project work on
- Automated Support for Classifying Software Failure
- Iterations Absoluteerror
- EECS 440: Machine Learning Programming Assignment 1 Assigned Wednesday September 2, due Monday September 28.
- Junction Trees: Motivation Standard algorithms (e.g., variable
- MaxPlanckInstitut fur biologische Kybernetik Max Planck Institute for Biological Cybernetics
- Machine Learning Applications in Bioinformatics
- Assigned Monday February 23, due Monday March 23. Turn in your code and writeup using the Digital Dropbox facility of blackboard. Please comment your code extensively so we can
- EECS 391/491: Introduction to AI (Spring 2009) Programming Assignment 3 Assigned Wednesday March 25, due Wednesday April 15. Turn in your code and writeup
