This page will be updated during the term.
Note: All topics allow for implementation-oriented projects. Those that are in addition well-suited for purely theoretical work are marked with (*).
(*) Teaching: for instance
Balbach. Measuring teachability using variants of the teaching dimension. Theoretical Computer Science 397:94-113, 2008.
(*) Sample compression: for instance
Floyd, Warmuth. Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Machine Learning 21:269-304, 1995.
(*) Active Learning: for instance
Balcan, Hanneke, Wortman. The true sample complexity of active learning. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 45-56, 2008.
(*) Stability of learning problems: for instance
Ben-David, von Luxburg, Pal. A sober look at clustering stability. Proceedings of the 19th Annual Conference on Learning Theory - COLT 2006, Lecture Notes in Computer Science 4005, pp. 5-19, Springer 2006.
Stability of machine learning methods: for instance
Dwyer, Holte. Decision tree instability and active learning. Proceedings of the 18th European Conference on Machine Learning - ECML 2007, Lecture Notes in Computer Science 4701, pp. 128-139, Springer 2007.
(*) Privacy-preserving learning: for instance
Kenthapadi, Mishra, Nissim. Simulatable auditing. Proceedings of the 24th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems - PODS 2005, pp. 118-127, ACM 2005.
Nabar, Marthi, Kenthapadi, Mishra, Motwani. Towards robustness in query auditing. Proceedings of the 32nd International Conference on Very Large Data Bases - VLDB 2006, pp. 151-162, ACM 2006.
Dwork. Differential privacy. Proceedings of the 33rd International Colloquium on Automata, Languages, and Programming - ICALP 2006, pp. 1-12, Springer 2006.
(*) Learning of hypothesis classes obeying a specific structure:
Learning of relational Bayesian networks: for instance
Getoor, Friedman, Koller, Pfeffer. Relational Data Mining. Springer 2001.
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Learning of dynamic Bayesian networks: for instance
Friedman, Murphy, Russell. Learning the Structure of Dynamic Probabilistic Networks. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 139-147, Morgan Kaufmann 1998.
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(*) Probabilistic circuits: for instance
Angluin, Aspnes, Chen, Eisenstat, Reyzin. Learning acyclic probabilistic circuits using test paths. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 169-180, 2008.
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(*) Finite automata: Many choices on this topic! One recent one for instance:
Angluin, Becerra-Bonache, Dediu, Reyzin. Learning finite automata using label queries. Proceedings of the 20th International Conference on Algorithmic Learning Theory - ALT 2009, Lecture Notes in Computer Science 5809, pp. 171-185, Springer 2009.
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(*) Social behaviour strategies: for instance
Kearns, Wortman. Learning from collective behavior. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 99-110, 2008.
Experimental work on interactive machine learning: for instance
Stumpf, Rajaram, Li, Wong, Burnett, Dietterich, Sullivan, Herlocker. Interacting meaningfully with machine learning systems: three experiments. International Journal of Human-Computer Studies 67:639-662, 2009.
Annual Conference on Learning Theory (COLT)
International Conference on Algorithmic Learning Theory (ALT)
International Conference on Machine Learning (ICML)
Neural Information Processing Systems (NIPS)
Machine Learning Journal (MLJ)
Journal of Machine Learning Research (JMLR)
Papers potentially giving rise to projects in CS 830
Note: All topics allow for implementation-oriented projects. Those that are in addition well-suited for purely theoretical work are marked with (*).
(*) Teaching: for instance
Balbach. Measuring teachability using variants of the teaching dimension. Theoretical Computer Science 397:94-113, 2008.
(*) Sample compression: for instance
Floyd, Warmuth. Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Machine Learning 21:269-304, 1995.
(*) Active Learning: for instance
Balcan, Hanneke, Wortman. The true sample complexity of active learning. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 45-56, 2008.
(*) Stability of learning problems: for instance
Ben-David, von Luxburg, Pal. A sober look at clustering stability. Proceedings of the 19th Annual Conference on Learning Theory - COLT 2006, Lecture Notes in Computer Science 4005, pp. 5-19, Springer 2006.
Stability of machine learning methods: for instance
Dwyer, Holte. Decision tree instability and active learning. Proceedings of the 18th European Conference on Machine Learning - ECML 2007, Lecture Notes in Computer Science 4701, pp. 128-139, Springer 2007.
(*) Privacy-preserving learning: for instance
Kenthapadi, Mishra, Nissim. Simulatable auditing. Proceedings of the 24th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems - PODS 2005, pp. 118-127, ACM 2005.
Nabar, Marthi, Kenthapadi, Mishra, Motwani. Towards robustness in query auditing. Proceedings of the 32nd International Conference on Very Large Data Bases - VLDB 2006, pp. 151-162, ACM 2006.
Dwork. Differential privacy. Proceedings of the 33rd International Colloquium on Automata, Languages, and Programming - ICALP 2006, pp. 1-12, Springer 2006.
(*) Learning of hypothesis classes obeying a specific structure:
Learning of relational Bayesian networks: for instance
Getoor, Friedman, Koller, Pfeffer. Relational Data Mining. Springer 2001.
---------
Learning of dynamic Bayesian networks: for instance
Friedman, Murphy, Russell. Learning the Structure of Dynamic Probabilistic Networks. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 139-147, Morgan Kaufmann 1998.
---------
(*) Probabilistic circuits: for instance
Angluin, Aspnes, Chen, Eisenstat, Reyzin. Learning acyclic probabilistic circuits using test paths. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 169-180, 2008.
---------
(*) Finite automata: Many choices on this topic! One recent one for instance:
Angluin, Becerra-Bonache, Dediu, Reyzin. Learning finite automata using label queries. Proceedings of the 20th International Conference on Algorithmic Learning Theory - ALT 2009, Lecture Notes in Computer Science 5809, pp. 171-185, Springer 2009.
---------
(*) Social behaviour strategies: for instance
Kearns, Wortman. Learning from collective behavior. Proceedings of the 21st Annual Conference on Learning Theory - COLT 2008, pp. 99-110, 2008.
Experimental work on interactive machine learning: for instance
Stumpf, Rajaram, Li, Wong, Burnett, Dietterich, Sullivan, Herlocker. Interacting meaningfully with machine learning systems: three experiments. International Journal of Human-Computer Studies 67:639-662, 2009.
Venues of interest for topics in CS 830
Annual Conference on Learning Theory (COLT)
International Conference on Algorithmic Learning Theory (ALT)
International Conference on Machine Learning (ICML)
Neural Information Processing Systems (NIPS)
Machine Learning Journal (MLJ)
Journal of Machine Learning Research (JMLR)