Balancing multiple objectives in contextual multi-armed bandits
Contextual multi-armed bandits are a popular framework for learning to make good decisions under uncertainty, and are applicable to areas ranging from ad placement to optimizing flu shot reminders. The majority of work in this space assumes the goal is to learn a decision policy to map from contexts to decisions in a way that maximizes the cumulative sum of outcomes of interest, such as total clicks or flu shot appointments. However in many real world settings there are multiple objectives of interest: for instance, a stakeholder may have a limited budget, care about fairness to subpopulations, or may wish to balance the experience for those participating in a study with the generalizable knowledge that might be learned and of use for other situations. In this talk I’ll discuss some of our work on learning to make decisions under uncertainty given multiple objectives of interest, and highlight motivating settings in education, healthcare and public policy.
Emma Brunskill is an associate professor in the Computer Science Department at Stanford University where she and Brunskill’s lab aim to create AI systems that learn from few samples to robustly make good decisions. Their work spans algorithmic and theoretical advances to experiments, inspired and motivated by the positive impact AI might have in education and healthcare. Brunskill’s lab is part of the Stanford AI Lab, the Stanford Statistical ML group, and AI Safety @Stanford. Brunskill has received a NSF CAREER award, Office of Naval Research Young Investigator Award, a Microsoft Faculty Fellow award and an alumni impact award from the computer science and engineering department at the University of Washington. Brunskill and her lab have received multiple best paper nominations and awards both for their AI and machine learning work (UAI best paper, Reinforcement Learning and Decision Making Symposium best paper twice) and for their work in Ai of education (Intelligent Tutoring Systems Conference, Educational Data Mining conference x3, CHI).