Machine learning has been successful and is prevalent in everyday life, shaping many aspects of modern society. Nevertheless, many fundamental questions remain, and it is important to develop a proper theoretical understanding of machine learning to guide its future development. In this talk I will discuss the fundamental properties of optimization, sampling, and game dynamics for machine learning.
In mobile health (mHealth), collecting momentary activity or behavioral state labels often comes at a significantly higher cost or level of user burden than collecting unlabeled data using passive sensing. This observation motivates the idea of attempting to optimize the collection of labeled data to minimize cost or burden when developing personalized models. In this talk, I will present on-going research on the problem of developing active learning methods for use in mobile health that leverage the affordances of this domain while respecting its unique constraints.
brings students and employers together for networking, education and connections. Twice each year, employers connect with Engineering and Computer Science students in an open, dynamic networking environment. Students come prepared with resumes to meet industry and tech representatives and learn about employment opportunities available, the characteristics employers seek, and also a realistic and insightful view of the job market and career paths for students interested in engineering and technical careers.
I will present the 'virtual democracy' framework for the design of ethical AI. In a nutshell, the framework consists of three steps: first, collect preferences from voters on example dilemmas; second, learn models of their preferences, which generalize to any (previously unseen) dilemma; and third, at runtime, predict the voters' preferences on the current dilemma, and aggregate these virtual 'votes' using a voting rule to reach a decision.