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Maytal Saar-Tsechansky
Person information
- affiliation: University of Texas at Austin, TX, USA
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2020 – today
- 2024
- [j18]Mathias Kraus
, Stefan Feuerriegel
Data-Driven Allocation of Preventive Care with Application to Diabetes Mellitus Type II. Manuf. Serv. Oper. Manag. 26(1): 137-153 (2024) - [i11]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 abs/2407.03474 (2024) - 2023
- [i10]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 abs/2302.02944 (2023) - [i9]Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky:
Mitigating Label Bias via Decoupled Confident Learning. CoRR abs/2307.08945 (2023) - [i8]Mathias Kraus, Stefan Feuerriegel, Maytal Saar-Tsechansky:
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II. CoRR abs/2308.06959 (2023) - 2022
- [c17]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: 133-146 - [i7]Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky:
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. CoRR abs/2207.07723 (2022) - [i6]Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky:
Algorithmic Fairness in Business Analytics: Directions for Research and Practice. CoRR abs/2207.10991 (2022) - [i5]Nicholas Wolczynski, Maytal Saar-Tsechansky, Tong Wang:
Learning to Advise Humans By Leveraging Algorithm Discretion. CoRR abs/2210.12849 (2022) - 2021
- [c16]Basil Maag, Stefan Feuerriegel, Mathias Kraus
Modeling longitudinal dynamics of comorbidities. CHIL 2021: 222-235 - [c15]Ruijiang Gao, Maytal Saar-Tsechansky
Human-AI Collaboration with Bandit Feedback. IJCAI 2021: 1722-1728 - [i4]Ruijiang Gao, Maytal Saar-Tsechansky, Maria De-Arteaga, Ligong Han, Min Kyung Lee, Matthew Lease:
Human-AI Collaboration with Bandit Feedback. CoRR abs/2105.10614 (2021) - [i3]Ruijiang Gao, Maytal Saar-Tsechansky:
Cost-Accuracy Aware Adaptive Labeling for Active Learning. CoRR abs/2105.11418 (2021) - [i2]Wanxue Dong, Maytal Saar-Tsechansky, Tomer Geva:
A Machine Learning Framework Towards Transparency in Experts' Decision Quality. CoRR abs/2110.11425 (2021) - 2020
- [c14]Ruijiang Gao, Maytal Saar-Tsechansky
Cost-Accuracy Aware Adaptive Labeling for Active Learning. AAAI 2020: 2569-2576 - [i1]Tong Wang, Maytal Saar-Tsechansky:
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness. CoRR abs/2011.08398 (2020)
2010 – 2019
- 2019
- [j17]Tomer Geva
More for less: adaptive labeling payments in online labor markets. Data Min. Knowl. Discov. 33(6): 1625-1673 (2019) - [j16]Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky:
Using Retweets When Shaping Our Online Persona: Topic Modeling Approach. MIS Q. 43(2) (2019) - [j15]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) - [c13]Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky:
Personality-Based Content Engineering for Rich Digital Media. Bled eConference 2019: 13 - 2018
- [j14]Markus Peters, Maytal Saar-Tsechansky
A scalable preference model for autonomous decision-making. Mach. Learn. 107(6): 1039-1068 (2018) - [j13]Wolfgang Ketter
Information Systems for a Smart Electricity Grid: Emerging Challenges and Opportunities. ACM Trans. Manag. Inf. Syst. 9(3): 10:1-10:22 (2018) - [c12]Haris Krijestorac, Rajiv Garg, Maytal Saar-Tsechansky:
The Role of Personality in the Diffusion of Digital Media. ICIS 2018 - 2017
- [j12]Meghana Deodhar, Joydeep Ghosh, Maytal Saar-Tsechansky, Vineet Keshari:
Active Learning with Multiple Localized Regression Models. INFORMS J. Comput. 29(3): 503-522 (2017) - [c11]Elad Liebman, Piyush Khandelwal, Maytal Saar-Tsechansky, Peter Stone:
Designing Better Playlists with Monte Carlo Tree Search. AAAI 2017: 4715-4720 - 2016
- [c10]Hilah Geva, Gal Oestreicher-Singer, Maytal Saar-Tsechansky:
Using Retweets to Shape our Online Persona: a Topic Modeling Approach. ICIS 2016 - [c9]Tomer Geva, Maytal Saar-Tsechansky:
Who's A Good Decision Maker? Data-Driven Expert Worker Ranking under Unobservable Quality. ICIS 2016 - 2015
- [c8]Elad Liebman, Maytal Saar-Tsechansky, Peter Stone:
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation. AAMAS 2015: 591-599 - 2014
- [j11]Danxia Kong, Maytal Saar-Tsechansky
Collaborative information acquisition for data-driven decisions. Mach. Learn. 95(1): 71-86 (2014) - 2013
- [j10]Markus Peters, Wolfgang Ketter
A reinforcement learning approach to autonomous decision-making in smart electricity markets. Mach. Learn. 92(1): 5-39 (2013) - 2012
- [c7]Markus Peters, Wolfgang Ketter
Autonomous Data-Driven Decision-Making in Smart Electricity Markets. ECML/PKDD (2) 2012: 132-147 - 2010
- [j9]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): 353-370 (2010)
2000 – 2009
- 2009
- [j8]Maytal Saar-Tsechansky, Prem Melville, Foster J. Provost:
Active Feature-Value Acquisition. Manag. Sci. 55(4): 664-684 (2009) - 2008
- [j7]Gary M. Weiss
Guest editorial: special issue on utility-based data mining. Data Min. Knowl. Discov. 17(2): 129-135 (2008) - 2007
- [j6]Maytal Saar-Tsechansky, Foster J. Provost:
Decision-Centric Active Learning of Binary-Outcome Models. Inf. Syst. Res. 18(1): 4-22 (2007) - [j5]Maytal Saar-Tsechansky, Foster J. Provost:
Handling Missing Values when Applying Classification Models. J. Mach. Learn. Res. 8: 1623-1657 (2007) - [c6]Foster J. Provost, Prem Melville, Maytal Saar-Tsechansky:
Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce. ICEC 2007: 389-398 - 2006
- [j4]Bianca Zadrozny, Gary M. Weiss
UBDM 2006: Utility-Based Data Mining 2006 workshop report. SIGKDD Explor. 8(2): 98-101 (2006) - [c5]David Pardoe, Peter Stone, Maytal Saar-Tsechansky, Kerem Tomak:
Adaptive mechanism design: a metalearning approach. ICEC 2006: 92-102 - 2005
- [j3]Gary M. Weiss
Report on UBDM-05: Workshop on Utility-Based Data Mining. SIGKDD Explor. 7(2): 145-147 (2005) - [c4]Prem Melville, Stewart M. Yang, Maytal Saar-Tsechansky, Raymond J. Mooney:
Active Learning for Probability Estimation Using Jensen-Shannon Divergence. ECML 2005: 268-279 - 2004
- [j2]Maytal Saar-Tsechansky, Foster J. Provost:
Active Sampling for Class Probability Estimation and Ranking. Mach. Learn. 54(2): 153-178 (2004) - [c3]Prem Melville, Maytal Saar-Tsechansky, Foster J. Provost, Raymond J. Mooney:
Active Feature-Value Acquisition for Classifier Induction. ICDM 2004: 483-486 - 2001
- [c2]Maytal Saar-Tsechansky, Nava Pliskin, Gadi Rabinowitz, Mark Tsechansky:
Patterns Extraction for Monitoring Medical Practices. HICSS 2001 - [c1]Maytal Saar-Tsechansky, Foster J. Provost:
Active Learning for Class Probability Estimation and Ranking. IJCAI 2001: 911-920
1990 – 1999
- 1999
- [j1]Maytal Saar-Tsechansky, Nava Pliskin
Mining relational patterns from multiple relational tables. Decis. Support Syst. 27(1-2): 177-195 (1999)
Coauthor Index
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