Maytal Saar-Tsechansky

51 papers A* 6A 1B 1C 5Journal 33Unranked 5
YearRankTypeTitle / Venue / Authors
2025 J jnl
A Machine Learning Framework for Assessing Experts' Decision Quality.
Manag. Sci.
Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva
2025 J jnl
Bias-Aware Mislabeling Detection via Decoupled Confident Learning.
CoRR
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
2024 J jnl
Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II.
Manuf. Serv. Oper. Manag.
Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
2024 J jnl
Fighting Bias with Bias: A Machine Learning Approach to Assess Human Bias.
CoRR
Wanxue Dong, Maria De-Arteaga, Maytal Saar-Tsechansky
2024 J jnl
How high-status women promote repeated collaboration among women in male-dominated contexts.
CoRR
Huimin Xu, Jamie Strassman, Ying Ding, Steven Gray, Maytal Saar-Tsechansky
2024 A* conf
SEL-BALD: Deep Bayesian Active Learning with Selective Labels.
NeurIPS
Ruijiang Gao, Mingzhang Yin, Maytal Saar-Tsechansky
2024 J jnl
The Value of AI Advice: Personalized and Value-Maximizing AI Advisors Are Necessary to Reliably Benefit Experts and Organizations.
CoRR
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
2023 J jnl
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II.
CoRR
Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky
2023 J jnl
Learning Complementary Policies for Human-AI Teams.
CoRR
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Wei Sun, Min Kyung Lee, Matthew Lease
2023 J jnl
Mitigating Label Bias via Decoupled Confident Learning.
CoRR
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
2022 J jnl
Algorithmic Fairness in Business Analytics: Directions for Research and Practice.
CoRR
Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky
2022 J jnl
Learning to Advise Humans By Leveraging Algorithm Discretion.
CoRR
Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang
2022 J jnl
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias.
CoRR
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
2022 B conf
When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias.
HCOMP
Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky
2021 J jnl
A Machine Learning Framework Towards Transparency in Experts' Decision Quality.
CoRR
Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva
2021 J jnl
Cost-Accuracy Aware Adaptive Labeling for Active Learning.
CoRR
Ruijiang Gao, Maytal Saar-Tsechansky
2021 A* conf
Human-AI Collaboration with Bandit Feedback.
IJCAI
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease
2021 J jnl
Human-AI Collaboration with Bandit Feedback.
CoRR
Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease
2021 — conf
Modeling longitudinal dynamics of comorbidities.
CHIL
Basil Maag, Stefan Feuerriegel, Mathias Kraus, Maytal Saar-Tsechansky, Thomas Züger
2020 J jnl
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness.
CoRR
Tong Wang, Maytal Saar-Tsechansky
2020 A* conf
Cost-Accuracy Aware Adaptive Labeling for Active Learning.
AAAI
Ruijiang Gao, Maytal Saar-Tsechansky
2019 J jnl
More for less: adaptive labeling payments in online labor markets.
Data Min. Knowl. Discov.
Tomer Geva, Maytal Saar-Tsechansky, Harel Lustiger
2019 — conf
Personality-Based Content Engineering for Rich Digital Media.
Bled eConference
Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky
2019 J jnl
The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling.
MIS Q.
Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
2019 J jnl
Using Retweets When Shaping Our Online Persona: Topic Modeling Approach.
MIS Q.
Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky
2018 J jnl
A scalable preference model for autonomous decision-making.
Mach. Learn.
Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes
2018 J jnl
Information Systems for a Smart Electricity Grid: Emerging Challenges and Opportunities.
ACM Trans. Manag. Inf. Syst.
Wolfgang Ketter, John Collins, Maytal Saar-Tsechansky, Ori Marom
2018 C conf
The Role of Personality in the Diffusion of Digital Media.
ICIS
Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky
2017 J jnl
Active Learning with Multiple Localized Regression Models.
INFORMS J. Comput.
Meghana Deodhar, Joydeep Ghosh, Maytal Saar-Tsechansky, Vineet Keshari
2017 A* conf
Designing Better Playlists with Monte Carlo Tree Search.
AAAI
Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone
2016 C conf
Using Retweets to Shape our Online Persona: a Topic Modeling Approach.
ICIS
Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky
2016 C conf
Who's A Good Decision Maker? Data-Driven Expert Worker Ranking under Unobservable Quality.
ICIS
Tomer Geva, Maytal Saar-Tsechansky
2015 A conf
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation.
AAMAS
Elad Liebman, Maytal Saar-Tsechansky, Peter Stone
2014 J jnl
Collaborative information acquisition for data-driven decisions.
Mach. Learn.
Danxia Kong, Maytal Saar-Tsechansky
2013 J jnl
A reinforcement learning approach to autonomous decision-making in smart electricity markets.
Mach. Learn.
Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins
2012 — conf
Autonomous Data-Driven Decision-Making in Smart Electricity Markets.
ECML/PKDD (2)
Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins
2010 J jnl
Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge.
INFORMS J. Comput.
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, Kerem Tomak
2009 J jnl
Active Feature-Value Acquisition.
Manag. Sci.
Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost
2008 J jnl
Guest editorial: special issue on utility-based data mining.
Data Min. Knowl. Discov.
Gary M. Weiss, Bianca Zadrozny, Maytal Saar-Tsechansky
2007 C conf
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce.
ICEC
Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky
2007 J jnl
Decision-Centric Active Learning of Binary-Outcome Models.
Inf. Syst. Res.
Maytal Saar-Tsechansky, Foster J. Provost
2007 J jnl
Handling Missing Values when Applying Classification Models.
J. Mach. Learn. Res.
Maytal Saar-Tsechansky, Foster J. Provost
2006 C conf
Adaptive mechanism design: a metalearning approach.
ICEC
David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Kerem Tomak
2006 J jnl
UBDM 2006: Utility-Based Data Mining 2006 workshop report.
SIGKDD Explor.
Bianca Zadrozny, Gary M. Weiss, Maytal Saar-Tsechansky
2005 — conf
Active Learning for Probability Estimation Using Jensen-Shannon Divergence.
ECML
Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney
2005 J jnl
Report on UBDM-05: Workshop on Utility-Based Data Mining.
SIGKDD Explor.
Gary M. Weiss, Maytal Saar-Tsechansky, Bianca Zadrozny
2004 A* conf
Active Feature-Value Acquisition for Classifier Induction.
ICDM
Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney
2004 J jnl
Active Sampling for Class Probability Estimation and Ranking.
Mach. Learn.
Maytal Saar-Tsechansky, Foster J. Provost
2001 A* conf
Active Learning for Class Probability Estimation and Ranking.
IJCAI
Maytal Saar-Tsechansky, Foster J. Provost
2001 — conf
Patterns Extraction for Monitoring Medical Practices.
HICSS
Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Mark Tsechansky
1999 J jnl
Mining relational patterns from multiple relational tables.
Decis. Support Syst.
Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Avi Porath