​​​
Bio: Maytal Saar-Tsechansky is an Associate Professor at The University of Texas at Austin whose research focuses on machine learning and data mining methods for data-driven business intelligence and decision-making. Her work introduced novel intelligent information acquisition methods, analysis of financial news to predict firms fundamental, and novel data mining techniques to improve health care.  Her research has been published in the top academic journals in business and computer science, including the Journal of Finance, Management Science, Information Systems Research, Journal of Machine Learning Research, and Machine Learning Journal, among other venues. Maytal’s research has been supported by both government and industry, including the National Science Foundation (NSF), SAP, and the Israeli Science Ministry. In recent years Maytal has served on the editorial boards of the Machine Learning Journal, the Information Systems Research (ISR) journal, the INFORMS Journal on Computing, and she is a frequent Program Committee member in the premier machine learning, data mining, and Information Systems conferences. At McCombs, Maytal has developed and taught courses on business intelligence with data mining.  She received her Ph.D. from New York University’s Stern School of Business and a B.S and M.S in Industrial Engineering from Ben Gurion University in Israel.



More for Less: Adaptive Labeling Cost Policy for Online Labor Markets

Abstract​: Online labor markets are being used extensively to facilitate the acquisition of human labels for supervised machine learning. In such markets the quality of labels that can be acquired at different costs vary across settings, and the particular relationship between cost and quality is generally unknown for any arbitrary setting. In this paper we develop a data-driven framework to iteratively adapt labelers’ pay so as to achieve a given level of the model’s performance at a lower cost. ​ 

​
​​
​​