The goal of this program is to grow the reach and impact of CS theory within machine learning.
One central component of the program will be formalizing basic questions in developing areas of practice, and gaining fundamental insights into these. Target areas of particular interest are interactive learning and representation learning. Interactive learning consists of scenarios in which the communication between human and learner is richer than a one-way transmission of labeled examples; this happens, for instance, in teaching, or explanation-based learning, and in crowdsourcing. Representation learning studies intermediate-
A second component of the program is advancing the algorithmic frontier of machine learning. Target areas include Bayesian statistics, in which many of the core algorithmic problems bear similarity to problems that have been studied intensively in the theoretical computer science community; and large-scale
A final component of the program is understanding heuristics: what works in practice, and why. The most popular algorithms for a variety of basic statistical tasks—
Long-Term Participants (including Organizers):
Visiting Graduate Students and Postdocs:
This program is now full and we are unfortunately unable to accommodate further participants (except for the workshops, for which registration opens approximately 10 weeks in advance).