Maytal Saar-Tsechansky

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 28

Top Topics

Labeling Effort

Preference Model

Top Venues

Publications

Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. Manuf. Serv. Oper. Manag. 26 (1) (2024)
Huimin Xu, Jamie Strassman, Ying Ding, Steven Gray, Maytal Saar-Tsechansky
How high-status women promote repeated collaboration among women in male-dominated contexts. CoRR (2024)
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
Mitigating Label Bias via Decoupled Confident Learning. CoRR (2023)
Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II. CoRR (2023)
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease
Learning Complementary Policies for Human-AI Teams. CoRR (2023)
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. HCOMP (2022)
Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
Algorithmic Fairness in Business Analytics: Directions for Research and Practice. CoRR (2022)
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. CoRR (2022)
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
Learning to Advise Humans By Leveraging Algorithm Discretion. CoRR (2022)
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease
Human-AI Collaboration with Bandit Feedback. IJCAI (2021)
Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva
A Machine Learning Framework Towards Transparency in Experts' Decision Quality. CoRR (2021)
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease
Human-AI Collaboration with Bandit Feedback. CoRR (2021)
Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger
Modeling longitudinal dynamics of comorbidities. CHIL (2021)
Ruijiang Gao, Maytal Saar-Tsechansky
Cost-Accuracy Aware Adaptive Labeling for Active Learning. CoRR (2021)
Ruijiang Gao, Maytal Saar-Tsechansky
Cost-Accuracy Aware Adaptive Labeling for Active Learning. AAAI (2020)
Tong Wang, Maytal Saar-Tsechansky
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness. CoRR (2020)
Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling. MIS Q. 43 (3) (2019)
Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky
Personality-Based Content Engineering for Rich Digital Media. Bled eConference (2019)
Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky
Using Retweets When Shaping Our Online Persona: Topic Modeling Approach. MIS Q. 43 (2) (2019)
Tomer Geva, Maytal Saar-Tsechansky, Harel Lustiger
More for less: adaptive labeling payments in online labor markets. Data Min. Knowl. Discov. 33 (6) (2019)
Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes
A scalable preference model for autonomous decision-making. Mach. Learn. 107 (6) (2018)
Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky
The Role of Personality in the Diffusion of Digital Media. ICIS (2018)
Wolfgang Ketter, John Collins, Maytal Saar-Tsechansky, Ori Marom
Information Systems for a Smart Electricity Grid: Emerging Challenges and Opportunities. ACM Trans. Manag. Inf. Syst. 9 (3) (2018)
Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone
Designing Better Playlists with Monte Carlo Tree Search. AAAI (2017)
Meghana Deodhar, Joydeep Ghosh, Maytal Saar-Tsechansky, Vineet Keshari
Active Learning with Multiple Localized Regression Models. INFORMS J. Comput. 29 (3) (2017)
Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky
Using Retweets to Shape our Online Persona: a Topic Modeling Approach. ICIS (2016)
Tomer Geva, Maytal Saar-Tsechansky
Who's A Good Decision Maker? Data-Driven Expert Worker Ranking under Unobservable Quality. ICIS (2016)
Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. AAMAS (2015)
Danxia Kong, Maytal Saar-Tsechansky
Collaborative information acquisition for data-driven decisions. Mach. Learn. 95 (1) (2014)
Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins
A reinforcement learning approach to autonomous decision-making in smart electricity markets. Mach. Learn. 92 (1) (2013)
Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins
Autonomous Data-Driven Decision-Making in Smart Electricity Markets. ECML/PKDD (2) (2012)
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, Kerem Tomak
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge. INFORMS J. Comput. 22 (3) (2010)
Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost
Active Feature-Value Acquisition. Manag. Sci. 55 (4) (2009)
Gary M. Weiss, Bianca Zadrozny, Maytal Saar-Tsechansky
Guest editorial: special issue on utility-based data mining. Data Min. Knowl. Discov. 17 (2) (2008)
Maytal Saar-Tsechansky, Foster J. Provost
Decision-Centric Active Learning of Binary-Outcome Models. Inf. Syst. Res. 18 (1) (2007)
Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC (2007)
Maytal Saar-Tsechansky, Foster J. Provost
Handling Missing Values when Applying Classification Models. J. Mach. Learn. Res. 8 (2007)
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Kerem Tomak
Adaptive mechanism design: a metalearning approach. ICEC (2006)
Bianca Zadrozny, Gary M. Weiss, Maytal Saar-Tsechansky
UBDM 2006: Utility-Based Data Mining 2006 workshop report. SIGKDD Explor. 8 (2) (2006)
Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney
Active Learning for Probability Estimation Using Jensen-Shannon Divergence. ECML (2005)
Gary M. Weiss, Maytal Saar-Tsechansky, Bianca Zadrozny
Report on UBDM-05: Workshop on Utility-Based Data Mining. SIGKDD Explor. 7 (2) (2005)
Maytal Saar-Tsechansky, Foster J. Provost
Active Sampling for Class Probability Estimation and Ranking. Mach. Learn. 54 (2) (2004)
Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney
Active Feature-Value Acquisition for Classifier Induction. ICDM (2004)
Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Mark Tsechansky
Patterns Extraction for Monitoring Medical Practices. HICSS (2001)
Maytal Saar-Tsechansky, Foster J. Provost
Active Learning for Class Probability Estimation and Ranking. IJCAI (2001)
Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Avi Porath
Mining relational patterns from multiple relational tables. Decis. Support Syst. 27 (1-2) (1999)