“Megastudy testing 25 treatments to reduce anti-democratic attitudes and partisan animosity”, Voelkel, J. G., et al. 

Science, 2024. 

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“SEL-BALD: Deep Bayesian Active Learning for Selective Labeling with Instance Rejection”, Ruijiang Gao, Mingzhang Yin, and Maytal Saar-Tsechansky,  NeurIPS, 2024.

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“A Machine Learning-based framework Towards Transparency in Experts Decision Quality”, Wanxue Dong, Maytal Saar-Tsechansky, and Tomer Geva. Management Science, 2024.

 

“Label Bias: A Pervasive and Invisibilized Problem”. Y. Li, M. De-Arteaga, and Maytal Saar-Tsechansky.

Accepted, Notices of the American Mathematical Society, 2024.

 

“Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II”, Mathias Kraus, Stefan Feuerriegel. And Maytal Saar-Tsechansky. Accepted, Manufacturing & Service Operations Management (M&SOM), 2023.

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"Learning to advise humans by leveraging algorithm discretion", Nick Wolczynski, Maytal Saar-Tsechansky, Tong Wang. arXiv preprint arXiv:2210.12849

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“Using Explainable AI to Understand Team Formation and Team Impact.” Huimin Xu, Maytal Saar-Tsechansky, Ying Ding, and Min Song. ACM Proceedings of the ASIS&T Annual Meeting, 2023.

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“Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals with Prediabetes or Diabetes: Retrospective Cohort Study”, with Züger Thomas, Simon Schallmoser, Stefan Feuerriegel, and Mathias Kraus.  Journal of Medical Internet Research, January 2023.

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Li, Y., De-Arteaga, M., and Saar-Tsechansky, M., “More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias.” Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022.

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“Algorithmic Fairness in Business Analytics: Directions for Research and Practice”, Maria De-Arteaga, Stefan Feuerriegel, and Maytal Saar-Tsechansky. Production and Operation Management, 2022.

“Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes”, with Züger Thomas, Simon Schallmoser, Stefan Feuerriegel, and  Mathias Kraus. Diabetes Technology and Therapeutics, 2022.

“Human-AI Collaboration with Bandit Feedback”, with Ruijiang Gao, Maria De-Arteaga, Matt Lease, and Min Kyung Lee. International Joint Conference on Artificial Intelligence (IJCAI-2021), 2021.

“Modeling Longitudinal Dynamics of Comorbidities”, Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger. The ACM Conference on Health, Inference, and Learning (CHIL), 2021.

"Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness", with Tong Wang, Best Paper Award, INFORMS Workshop on Data Science, 2020.
NeurIPS2020, ​Algorithmic Fairness through the Lens of Causality and Interpretability

“Who is a Better Decision Maker? Data-Driven Expert Ranking under Unobservable Quality”, with Tomer Geva. Production and Operations Management, 2020. (https://doi.org/10.1111/poms.13260)

“Cost-Accuracy Aware Adaptive Labeling for Active Learning”,  Ruijiang Gao & Maytal Saar-Tsechansky, Accepted, AAAI Conference  on Artificial Intelligence (AAAI 2020).

"Personality-Based Content Engineering for Rich Digital Media”, Haris Krijestorac, Rajiv Garg, and Maytal Saar-Tsechansky,  Conference on Information Systems & Technology (CIST), 2019.

“More For Less: Adaptive Labeling Payment for Online Labor Markets”. Tomer Geva, Maytal Saar-Tsechansky, & Harel Lustiger. Data Mining and Knowledge Discovery, 2019 (https://doi.org/10.1007/s10618-019-00637-z).

“Using retweets to shape our online persona: a topic modeling approach”, Hilah Levin, Gal Oestreicher-Singer, and Maytal Saar-Tsechansky. Management Information Systems Quarterly (MISQ, 2019).

"The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling”, Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone. Management Information Systems Quarterly (MISQ), 2019.

"A Scalable Preference Model for Autonomous Decision-Making Involving Consumer Choices”, Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead Williamson, Perry Groot, and Tom Heskes, Machine Learning, 2018, 107: 1039  (https://doi.org/10.1007/s10994-018-5705-5

“Information Systems for a Sustainable Smart Electricity Grid”, Wolfgang Ketter, John Collins, Ori Marom, and Maytal Saar-Tsechansky.  Accepted, ACM Transactions on Management Information Systems (TMIS), 2018.

"Active Learning with Local Models for Large Heterogeneous, Dyadic Data”, with Meghana Deodhar, and Joydeep Ghosh. INFORMS Journal on Computing, Vol 9:3, pp 503-522.  2017.

“Designing Better Playlists with Monte Carlo Tree Search", Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, and Peter Stone. The Twenty-Ninth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-17), 2017 (25% acceptance rate).

“Who is a Good Decision Maker? Data-Driven Expert Ranking under Unobservable Quality”, with Tomer Geva, International Conference on Information Systems, 2016.

Using Retweets to Shape our Online Persona: A Topic Modeling Approach", with Hilah Geva and Gal Oestreicher-Singer, International Conference on Information Systems, 2016.

“The Business of Business Data Science”, Maytal Saar-Tsechansky, Editorial, Management of Information Systems Quarterly (MISQ), 2015.

“DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation”, Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone. Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), 2015. (Top-tier Artificial Intelligence conference, 24% acceptance).

Danxia Kong and Maytal Saar-Tsechansky. Collaborative Information Acquisition for Data-Driven Decisions”. 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 Feature-Value Acquisition”. 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, 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.