Learning for Decision Making: A Tale of Complex Human Preferences
Carnegie Mellon University
Leqi Liu is a Ph.D. candidate in the Machine Learning Department at Carnegie Mellon University, where she is advised by Zachary Lipton. Her research revolves around machine learning and behavioral sciences, with a focus on developing a theory for building learning systems that interact with people. She is a recipient of the Open Philanthropy AI Fellowship (2020-2023) and has interned at Apple and DeepMind during her Ph.D.
AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI
Machine learning systems are deployed in diverse decision-making settings in service of stakeholders characterized by complex preferences. For example, in healthcare and finance, we ought to account for various levels of risk tolerance; and in personalized recommender systems, we face users whose preferences evolve dynamically over time. Building systems better aligned with stakeholder needs requires that we take the rich nature of human preferences into account. In this talk, I will give an overview of my research on the statistical and algorithmic foundations for building such human-centered machine learning systems. First, I will present a line of work that draws inspiration from the economics literature to develop learning algorithms that account for the risk preferences of stakeholders. Subsequently, I will discuss a line of work that draws insights from the psychology literature to develop online learning algorithms for personalized recommender systems that account for users’ evolving preferences.
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