Learning for Decision Making: A Tale of Complex Human Preferences
Leqi Liu
Carnegie Mellon University
14 Feb 2023, 2:00 pm - 3:00 pm
Virtual talk
SWS Colloquium
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. ...
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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