Online feature elicitation in interactive optimization

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 73-80

  Boutilier, Craig ^a   Regan, Kevin ^a   Viappiani, Paolo ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL PROCEDURES; CONCEPT LEARNING; DECISION SUPPORTS; INTERACTIVE OPTIMIZATION; MINIMAX REGRET; OPTIMAL ALTERNATIVE; QUERY STRATEGIES; UTILITY FUNCTIONS;

DECISION SUPPORT SYSTEMS; LIBRARIES; ROBOT LEARNING;

OPTIMIZATION;

EID: 71149097864     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Angluin, D. (1987). Queries and concept learning. Machine Learning, 2, 319-342.
2. Blum, A., Jackson, J. C., Sandholm, T., & Zinkevich, M. (2004). Preference elicitation and query learning. Journal of Machine Learning Research, 5, 649-667.
3. Boutilier, C., Patrascu, R., Poupart, P., & Schuurmans, D. (2006). Constraint-based optimization and utility elicitation using the minimax decision criterion. Artifical Intelligence, 170, 686-713.
4. Braziunas, D., & Boutilier, C. (2007). Minimax regret-based elicitation of generalized additive utilities. Proc. 23rd Conf. on Uncertainty in AI (UAI-07) (pp. 25-32). Vancouver.
5. Cohn, D., Atlas, L., & Ladner, R. (1994). Improving generalization with active learning. Machine Learning, 25, 201-221.
6. Dasgupta, S., Hsu, D., & Monteleoni, C. (2008). A general agnostic active learning algorithm. Advances in Neural Information Processing Systems 20 (pp. 353-360). Cambridge, MA: MIT Press.
7. Fishburn, P. C. (1967). Interdependence and additivity in multivariate, unidimensional expected utility theory. International Economic Review, 8, 335-342.
8. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28, 133-168.
9. Haussler, D. (1988). Quantifying inductive bias: Ai learning algorithms and valiant's learning framework. Artificial Intelligence, 36, 177-221.
10. Hellerstein, L., Pillaipakkamnatt, K., Raghavan, V. V., & Wilkins, D. (1996). How many queries are needed to learn? Journal of the ACM, 43, 840-862.
11. Hirsh, H. (1992). Polynomial-time learning with version spaces. Proc. 10th National Conf. on AI (AAAI-92) (pp. 117-122). San Jose.
12. Mitchell, T. M. (1977). Version spaces: A candidate elimination approach to rule learning. Proc. 5th Intl. Joint Conf. on AI (IJCAI-77) (pp. 305-310). Cambridge.
13. Salo, A., & Hämäläinen, R. P. (2001). Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information. IEEE Trans. on Sys., Man and Cyber., 31, 533-545.
14. Savage, L. J. (1954). The foundations of statistics. New York: Wiley.