Maytal Saar-Tsechansky

According to our database, Maytal Saar-Tsechansky authored at least 43 papers between 1999 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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., January, 2024

How high-status women promote repeated collaboration among women in male-dominated contexts.

Huimin Xu, Jamie Strassman, Ying Ding, Steven Gray, Maytal Saar-Tsechansky

CoRR, 2024

2023

Mitigating Label Bias via Decoupled Confident Learning.

Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

CoRR, 2023

Learning Complementary Policies for Human-AI Teams.

Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease

CoRR, 2023

2022

Learning to Advise Humans By Leveraging Algorithm Discretion.

Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang

CoRR, 2022

Algorithmic Fairness in Business Analytics: Directions for Research and Practice.

Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky

CoRR, 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

When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias.

Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, 2022

2021

A Machine Learning Framework Towards Transparency in Experts' Decision Quality.

Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva

CoRR, 2021

Human-AI Collaboration with Bandit Feedback.

Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Modeling longitudinal dynamics of comorbidities.

Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness.

Tong Wang, Maytal Saar-Tsechansky

CoRR, 2020

Cost-Accuracy Aware Adaptive Labeling for Active Learning.

Ruijiang Gao, Maytal Saar-Tsechansky

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling.

Elad Liebman, Maytal Saar-Tsechansky, Peter Stone

MIS Q., 2019

Using Retweets When Shaping Our Online Persona: Topic Modeling Approach.

Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky

MIS Q., 2019

More for less: adaptive labeling payments in online labor markets.

Tomer Geva, Maytal Saar-Tsechansky, Harel Lustiger

Data Min. Knowl. Discov., 2019

Personality-Based Content Engineering for Rich Digital Media.

Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky

Proceedings of the 32nd Bled eConference: Humanizing Technology for a Sustainable Society, 2019

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

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

The Role of Personality in the Diffusion of Digital Media.

Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky

Proceedings of the International Conference on Information Systems, 2018

2017

Active Learning with Multiple Localized Regression Models.

Meghana Deodhar, Joydeep Ghosh, Maytal Saar-Tsechansky, Vineet Keshari

INFORMS J. Comput., 2017

Designing Better Playlists with Monte Carlo Tree Search.

Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Who's A Good Decision Maker? Data-Driven Expert Worker Ranking under Unobservable Quality.

Tomer Geva, Maytal Saar-Tsechansky

Proceedings of the International Conference on Information Systems, 2016

Using Retweets to Shape our Online Persona: a Topic Modeling Approach.

Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky

Proceedings of the International Conference on Information Systems, 2016

2015

DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation.

Elad Liebman, Maytal Saar-Tsechansky, Peter Stone

Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015

2014

Collaborative information acquisition for data-driven decisions.

Danxia Kong, Maytal Saar-Tsechansky

Mach. Learn., 2014

2013

A reinforcement learning approach to autonomous decision-making in smart electricity markets.

Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins

Mach. Learn., 2013

2012

Autonomous Data-Driven Decision-Making in Smart Electricity Markets.

Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2010

Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge.

David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, Kerem Tomak

INFORMS J. Comput., 2010

2009

Active Feature-Value Acquisition.

Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost

Manag. Sci., 2009

2008

Guest editorial: special issue on utility-based data mining.

Gary M. Weiss, Bianca Zadrozny, Maytal Saar-Tsechansky

Data Min. Knowl. Discov., 2008

2007

Handling Missing Values when Applying Classification Models.

Maytal Saar-Tsechansky, Foster J. Provost

J. Mach. Learn. Res., 2007

Decision-Centric Active Learning of Binary-Outcome Models.

Maytal Saar-Tsechansky, Foster J. Provost

Inf. Syst. Res., 2007

Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce.

Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky

Proceedings of the 9th International Conference on Electronic Commerce: The Wireless World of Electronic Commerce, 2007

2006

UBDM 2006: Utility-Based Data Mining 2006 workshop report.

Bianca Zadrozny, Gary M. Weiss, Maytal Saar-Tsechansky

SIGKDD Explor., 2006

Adaptive mechanism design: a metalearning approach.

David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Kerem Tomak

Proceedings of the 8th International Conference on Electronic Commerce: The new e-commerce, 2006

2005

Report on UBDM-05: Workshop on Utility-Based Data Mining.

Gary M. Weiss, Maytal Saar-Tsechansky, Bianca Zadrozny

SIGKDD Explor., 2005

Active Learning for Probability Estimation Using Jensen-Shannon Divergence.

Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney

Proceedings of the Machine Learning: ECML 2005, 2005

2004

Active Sampling for Class Probability Estimation and Ranking.

Maytal Saar-Tsechansky, Foster J. Provost

Mach. Learn., 2004

Active Feature-Value Acquisition for Classifier Induction.

Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney

Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004

2001

Active Learning for Class Probability Estimation and Ranking.

Maytal Saar-Tsechansky, Foster J. Provost

Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001

Patterns Extraction for Monitoring Medical Practices.

Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Mark Tsechansky

Proceedings of the 34th Annual Hawaii International Conference on System Sciences (HICSS-34), 2001

1999

Mining relational patterns from multiple relational tables.

Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Avi Porath

Decis. Support Syst., 1999