| Gary Weiss, Bianca Zadrozny, and Maytal Saar-Tsechansky. Utility-Based Data Mining, Editorial: Special Issue on Utility Based Data Mining. Journal of Data Mining and Knowledge Discovery, forthcoming. |
| David Pardo, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, and Kerem Tomak. 2010. Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge. INFORMS Journal on Computing 22(3), 353-370. |
| Maytal Saar-Tsechansky, Prem Melville, and Foster Provost. 2009. Active Feature-Value Acquisition. Management Science 55, 664-684. |
| Maytal Saar-Tsechansky and Foster Provost. 2008. Handling Missing Values when Applying Classification Models. Journal of Machine Learning Research 9, 1625-1657. |
| Paul C Tetlock, Maytal Saar-Tsechansky, and Macskassy, Sofus. 2008. More Than Words: Quantifying Language to Measure Firms' Fundamentals. Journal of Finance 63, 1437-1467. |
| Provost, Foster, Melville, Prem, and Maytal Saar-Tsechansky. 2007. Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce, in The Proceedings of The Ninth International Conference on Electronic Commerce, Minneapolis, Minnesota. |
| Maytal Saar-Tsechansky and Provost, Foster. 2007. Decision-Centric Active Learning of Binary-Outcome Models. Information Systems Research 18, 4-22. |
| Pardoe, David, Sone, Peter, Maytal Saar-Tsechansky, and Tomak, Kerem. 2006. Adaptive Mechanism Design: A Metalearning Approach, in The Proceedings of The Eighth International Conference on Electronic Commerce, |
| Melville, Prem, Yang, Stewart M., Maytal Saar-Tsechansky, and Mooney, Raymond J. 2005. Active Learning for Probability Estimation using Jensen-Shannon Divergence, in The Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal: ECML 2005. |
| Pardoe, David, Stone, Peter, Maytal Saar-Tsechansky, and Tomak, Kerem. 2005. Adaptive Auctions: Learning to Adjust to Bidders, in Workshop on Information Technologies and Systems (WITS), 2005. |
| Melville, P., Maytal Saar-Tsechansky, Provost, F., and Mooney, R.J.. 2005. An Expected Utility Approach to Active Feature-value Acquisition, in The Proceedings of the Fifth International Conference on Data Mining (ICDM-2005), Houston, TX. |
| Melville, P., Maytal Saar-Tsechansky, Provost, F., and Mooney, R.J.. 2005. Economical Active Feature-value Acquisition through Expected Utility Estimation, in Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL. |
| Maytal Saar-Tsechansky and Chen, Hsuan-Wei Michelle. 2005. Variance-Based Active Learning for Classifier Induction, in Workshop on Information Technologies and Systems (WITS), WITS 2005. |
| Melville, Prem, Maytal Saar-Tsechansky, Provost, Foster, and Mooney, Raymond J.. 2004. Active Feature Acquisition for Classifier Induction, in The Proceedings of The Fourth International Conference on Data Mining (ICDM-2004), Bighton, UK. |
| Maytal Saar-Tsechansky and Foster Provost. 2004. Active Sampling for Class Probability Estimation and Ranking. Machine Learning 54 (2), 153-178. |
| Maytal Saar-Tsechansky and Provost, Foster. 2001. Active Learning for Class Probability Estimation and Ranking, in Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle, WA. |
| Maytal Saar-Tsechansky, Nava Pliskin, Gad Rabinowitz, and Mark Tsechansky. 2001. Monitoring Quality of Care with Relational Patterns. Topics in Health Information Management 22, 24-35. |
| Maytal Saar-Tsechansky, Nava Pliskin, Gad Rabinowitz, and Mark Tsechansky. 2001. Pattern Extraction for Monitoring Medical Practices, in Proceedings of the 34th Hawaii International Conference on Systems Sciences (HICSS), Maui, HI: IEEE Computer Society Press. |
| Maytal Saar-Tsechansky, Nava, Pliskin, Gadi, Rabinowitz, and Avi, Porath. 1999. Mining Relational Patterns from Multiple Relational Tables. Decision Support Systems 27, 177-195. |