|• “DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation”, with Elad Liebman and Peter Stone. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015. (24% acceptance). pdf.
• “Active Learning with Local Models for Large Heterogeneous, Dyadic Data”. With Meghana Deodhar, and Joydeep Ghosh. 2014.
• Collaborative Information Acquisition for Data-Driven Decisions”, with Danxia Kong. Machine Learning, (2014), Volume 95, Issue 1, pages 71-86.
• “A Reinforcement Learning Approach to Autonomous Decision-Making in Smart Electricity Markets", With Markus Peters, Wolf Ketter, and John Collins. Machine Learning, (2013) 92:5–39.
• “Automated data-driven tariff pricing for the Smart Grid”, INFORMS Conference on Information Systems and Technology (CIST 2012).
• Autonomous data-driven decision-making in Smart Electricity Markets, The European Conference on Machine Learning (The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases), ECML-PKDD 2012, with Markus Peters, Wolf Ketter, and John Collins, 2012. (23% acceptance rate.).
• Selective Data Acquisition for Machine Learning. J. Attenberg, P. Melville, F. Provost, and M. Saar-Tsechansky. In B. Krishnapuram, S. Yu, B. Rao (eds.), “Cost-Sensitive Machine Learning”, 2012.
• Claudia Perlich, Maytal Saar-Tsechansky, Wojciech Gryc, Mary Helander, Rick Lawrence, Yan Liu, Chandan Reddy, Saharon Rosset. “On Data-Driven Analysis of User-Generated Content”. Invited article, IEEE Intelligent Systems 25(1) (2010) 12-17
• David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, and Kerem Tomak , “Data-Driven Auction Design And the Incorporation of Prior Knowledge”, INFORMS Journal on Computing, Vol. 22, No. 3, pp. 353–370, 2010.
• Danxia Kong and Maytal Saar-Tsechansky, A Framework for Collaborative Information Acquisition Policies, Workshop on Information Technology (WITS), 2010.
• Danxia Kong and Maytal Saar-Tsechansky, Collaborative Information Acquisition, Budgeted Learning Workshop, ICML 2010 (International Conference on Machine Learning), 2010.
• Maytal Saar-Tsechansky, Prem Melville and Foster Provost, “Active Information Acquisition for Model Induction”. Management Science, 55( 4), pp. 664–684, 2009.
• Paul Tetlock, Maytal Saar-Tsechansky and Sofus Macskassy. “More Than Words: Quantifying Language to Measure Firms' Fundamentals”. Journal of Finance, 63, 1437-1467, 2008.
• Maytal Saar-Tsechansky and Foster Provost. “Decision-centric Active Learning of Binary-Outcome Models”, Information Systems Research, Vol. 18, No. 1, pp. 1–19, 2007.
• Maytal Saar-Tsechansky and Foster Provost. “Handling Missing Values When Applying Classification Models”. Journal of Machine Learning Research, 8(Jul):1623--1657, 2007.
• Foster Provost, Prem Melville, and Maytal Saar-Tsechansky. Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. Invited paper to appear In the Proceedings of The Ninth International Conference on Electronic Commerce (EC 2007), Minneapolis, 2007.
• Saar-Tsechansky, Duy Vu, Mikhail Bilenko, and Prem Melville. “Intelligent Information Acquisition for Improved Clustering”, Workshop on Information Technologies and Systems (WITS), 2007.
• David Pardoe, Peter Stone, Maytal Saar-Tsechansky, and Kerem Tomak, “Adaptive Mechanism Design: A Metalearning Approach”. In the Proceedings of The Eighth International Conference on Electronic Commerce, 2006.
• Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, and Raymond J. Mooney. “Active Learning for Probability Estimation using Jensen-Shannon Divergence”, The Proceedings of The 16th European Conference on Machine Learning (ECML), 2005. 10% acceptance rate.
• Melville, P., Saar-Tsechansky, M., Provost, F. and Mooney, R.J. An Expected Utility Approach to Active Feature-value Acquisition. The Proceedings of the Fifth International Conference on Data Mining (ICDM), 2005. 13% acceptance rate.
• David Pardoe, Peter Stone, Maytal Saar-Tsechansky and Kerem Tomak. Adaptive Auctions: Learning to Adjust to Bidders. Workshop on Information Technologies and Systems (WITS), 2005. 27% acceptance rate.
• Melville, P., Saar-Tsechansky, M., Provost, F. and Mooney, R.J. Economical Active Feature-value Acquisition through Expected Utility Estimation. Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL, August 2005.
• Maytal Saar-Tsechansky and Hsuan Wei-Chen. Variance-Based Active Learning for Classifier Induction. Workshop on Information Technologies and Systems (WITS), 2005. 27% acceptance rate.
• Maytal Saar-Tsechansky and Foster Provost. “Active Sampling for Class Probability Estimation and Ranking.” Machine Learning, 54:2, 153-178, 2004.
• Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney. “Active Feature Acquisition for Classifier Induction.” The Proceedings of The Fourth International Conference on Data Mining (ICDM), 2004. 14% acceptance rate.
• Saar-Tsechansky Maytal and Provost Foster. “Active Learning for Class Probability Estimation and Ranking” The Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), 2001. 24% acceptance rate. (An extended version was published in the Machine Learning Journal)
• Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz., Avi Porath, and Mark Tsechansky, "Monitoring Quality of Care with Relational Patterns". Topics in Health Information Management, Vol. 22, N0. 1, 2001.
• Saar-Tsechansky Maytal, Pliskin Nava, Rabinowitz Gadi, and Tsechansky Mark. "Patterns Extraction for Monitoring Medical Practices," Proceedings of the 34th Hawaii International Conference on Systems Sciences (HICSS). IEEE Computer Society Press, 2001. Best Paper Award, Information Technology in Health Care Track.
• Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, and Avi Porath, "Mining Relational Patterns from Multiple Relational Tables," Decision Support Systems, Vol. 27, No. 1-2, 177-195, 1999.
• Gary M. Weiss, Maytal Saar-Tsechansky, and Bianca Zadrozny (guest editors). Special Issue on Utility-Based Data Mining, Data Mining and Knowledge Discovery, 17(2), October 2008.
• Bianca Zadrozny, Gary M. Weiss and Maytal Saar-Tsechansky (editors). Proceedings of the ACM SIGKDD, International Workshop on Utility-Based Data Mining, ACM Press, August, 2006.
• Gary M. Weiss, Maytal Saar-Tsechansky and Bianca Zadrozny (editors). Proceedings of the ACM SIGKDD, International Workshop on Utility-Based Data Mining, ACM Press, August 2005.
• “The Influence of CEOs’ Positive Affect on Analysts’ Forecasts”, Melanie Milovac, Jochen Menges, Maytal Saar-Tsechansky, and Thomas Graeber. Under review, first round, Administrative Science Quarterly (ASQ).
• “A Scalable Preference Model for Autonomous Decision-Making Involving Consumer Choices”, Markus Peters, Perry Groot, Wolfgang Ketter, Maytal Saar-Tsechansky, and Tom Heske.
• “A Reinforcement-Learning Framework for Music Playlist Recommendation.”, with Elad Liebman and Peter Stone. Working paper.
• “On Energy Information Systems”, with Wolf Ketter, John Collins, and Ori Marom, Under review, MISQ.