Mining relational patterns from multiple relational tables. - Maytal Saar-Tsechansky; Nava Pliskin; Gadi Rabinowitz; Avi Porath (Decis. Support Syst., 1999)

Patterns Extraction for Monitoring Medical Practices. - Maytal Saar-Tsechansky; Nava Pliskin; Gadi Rabinowitz; Mark Tsechansky (HICSS, 2001)

Active Learning for Class Probability Estimation and Ranking. - Maytal Saar-Tsechansky; Foster J. Provost (IJCAI, 2001)

Active Feature-Value Acquisition for Classifier Induction. - Prem Melville; Maytal Saar-Tsechansky; Foster J. Provost; Raymond J. Mooney (ICDM, 2004)

Active Sampling for Class Probability Estimation and Ranking. - Maytal Saar-Tsechansky; Foster J. Provost (Mach. Learn., 2004)

Active Learning for Probability Estimation Using Jensen-Shannon Divergence. - Prem Melville; Stewart M. Yang; Maytal Saar-Tsechansky; Raymond J. Mooney (ECML, 2005)

Report on UBDM-05: Workshop on Utility-Based Data Mining. - Gary M. Weiss; Maytal Saar-Tsechansky; Bianca Zadrozny (SIGKDD Explor., 2005)

Adaptive mechanism design: a metalearning approach. - David Pardoe; Peter Stone 001; Maytal Saar-Tsechansky; Kerem Tomak (ICEC, 2006)

UBDM 2006: Utility-Based Data Mining 2006 workshop report. - Bianca Zadrozny; Gary M. Weiss; Maytal Saar-Tsechansky (SIGKDD Explor., 2006)

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. - Foster J. Provost; Prem Melville; Maytal Saar-Tsechansky (ICEC, 2007)

Decision-Centric Active Learning of Binary-Outcome Models. - Maytal Saar-Tsechansky; Foster J. Provost (Inf. Syst. Res., 2007)

Handling Missing Values when Applying Classification Models. - Maytal Saar-Tsechansky; Foster J. Provost (J. Mach. Learn. Res., 2007)

Guest editorial: special issue on utility-based data mining. - Gary M. Weiss; Bianca Zadrozny; Maytal Saar-Tsechansky (Data Min. Knowl. Discov., 2008)

Active Feature-Value Acquisition. - Maytal Saar-Tsechansky; Prem Melville; Foster J. Provost (Manag. Sci., 2009)

Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge. - David Pardoe; Peter Stone 001; Maytal Saar-Tsechansky; Tayfun Keskin; Kerem Tomak (INFORMS J. Comput., 2010)

Autonomous Data-Driven Decision-Making in Smart Electricity Markets. - Markus Peters; Wolfgang Ketter; Maytal Saar-Tsechansky; John Collins (ECML/PKDD (2), 2012)

A reinforcement learning approach to autonomous decision-making in smart electricity markets. - Markus Peters; Wolfgang Ketter; Maytal Saar-Tsechansky; John Collins (Mach. Learn., 2013)

Collaborative information acquisition for data-driven decisions. - Danxia Kong; Maytal Saar-Tsechansky (Mach. Learn., 2014)

DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. - Elad Liebman; Maytal Saar-Tsechansky; Peter Stone 001 (AAMAS, 2015)

Using Retweets to Shape our Online Persona: a Topic Modeling Approach. - Hilah Geva; Gal Oestreicher-Singer; Maytal Saar-Tsechansky (ICIS, 2016)

Who's A Good Decision Maker? Data-Driven Expert Worker Ranking under Unobservable Quality. - Tomer Geva; Maytal Saar-Tsechansky (ICIS, 2016)

Designing Better Playlists with Monte Carlo Tree Search. - Elad Liebman; Piyush Khandelwal; Maytal Saar-Tsechansky; Peter Stone 001 (AAAI, 2017)

Active Learning with Multiple Localized Regression Models. - Meghana Deodhar; Joydeep Ghosh; Maytal Saar-Tsechansky; Vineet Keshari (INFORMS J. Comput., 2017)

The Role of Personality in the Diffusion of Digital Media. - Haris Krijestorac; Rajiv Garg; Maytal Saar-Tsechansky (ICIS, 2018)

A scalable preference model for autonomous decision-making. - Markus Peters; Maytal Saar-Tsechansky; Wolfgang Ketter; Sinead A. Williamson; Perry Groot; Tom Heskes (Mach. Learn., 2018)

Information Systems for a Smart Electricity Grid: Emerging Challenges and Opportunities. - Wolfgang Ketter; John Collins; Maytal Saar-Tsechansky; Ori Marom (ACM Trans. Manag. Inf. Syst., 2018)

Personality-Based Content Engineering for Rich Digital Media. - Haris Krijestorac; Rajiv Garg; Maytal Saar-Tsechansky (Bled eConference, 2019)

More for less: adaptive labeling payments in online labor markets. - Tomer Geva; Maytal Saar-Tsechansky; Harel Lustiger (Data Min. Knowl. Discov., 2019)

Using Retweets When Shaping Our Online Persona: Topic Modeling Approach. - Hilah Geva; Gal Oestreicher-Singer; Maytal Saar-Tsechansky (MIS Q., 2019)

The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling. - Elad Liebman; Maytal Saar-Tsechansky; Peter Stone 001 (MIS Q., 2019)

Cost-Accuracy Aware Adaptive Labeling for Active Learning. - Ruijiang Gao; Maytal Saar-Tsechansky (AAAI, 2020)

Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness. - Tong Wang; Maytal Saar-Tsechansky (CoRR, 2020)

Modeling longitudinal dynamics of comorbidities. - Basil Maag; Stefan Feuerriegel; Mathias Kraus; Maytal Saar-Tsechansky; Thomas Züger (CHIL, 2021)

Human-AI Collaboration with Bandit Feedback. - Ruijiang Gao; Maytal Saar-Tsechansky; Maria De-Arteaga; Ligong Han; Min Kyung Lee; Matthew Lease (IJCAI, 2021)

A Machine Learning Framework Towards Transparency in Experts' Decision Quality. - Wanxue Dong; Maytal Saar-Tsechansky; Tomer Geva (CoRR, 2021)

When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (HCOMP, 2022)

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2022)

Algorithmic Fairness in Business Analytics: Directions for Research and Practice. - Maria De-Arteaga; Stefan Feuerriegel; Maytal Saar-Tsechansky (CoRR, 2022)

Learning to Advise Humans By Leveraging Algorithm Discretion. - Nicholas Wolczynski; Maytal Saar-Tsechansky; Tong Wang (CoRR, 2022)

Learning Complementary Policies for Human-AI Teams. - Ruijiang Gao; Maytal Saar-Tsechansky; Maria De-Arteaga; Ligong Han; Wei Sun 031; Min Kyung Lee; Matthew Lease (CoRR, 2023)

Mitigating Label Bias via Decoupled Confident Learning. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2023)

SEL-BALD: Deep Bayesian Active Learning with Selective Labels. - Ruijiang Gao; Mingzhang Yin; Maytal Saar-Tsechansky (NeurIPS, 2024)

How high-status women promote repeated collaboration among women in male-dominated contexts. - Huimin Xu; Jamie Strassman; Ying Ding 001; Steven Gray; Maytal Saar-Tsechansky (CoRR, 2024)

Fighting Bias with Bias: A Machine Learning Approach to Assess Human Bias. - Wanxue Dong; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2024)

The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations. - Nicholas Wolczynski; Maytal Saar-Tsechansky; Tong Wang (CoRR, 2024)

Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. - Mathias Kraus; Stefan Feuerriegel; Maytal Saar-Tsechansky (Manuf. Serv. Oper. Manag., 2024)

A Machine Learning Framework for Assessing Experts' Decision Quality. - Wanxue Dong; Maytal Saar-Tsechansky; Tomer Geva (Manag. Sci., 2025)

Bias-Aware Mislabeling Detection via Decoupled Confident Learning. - Yunyi Li; Maria De-Arteaga; Maytal Saar-Tsechansky (CoRR, 2025)