Learning to Vote: A research project on learning of voting rules, especially on fairness criterion, and the multi-round winners determination procedure.
Learning to Rank: A library of learning to rank algorithms for information retrieval and ranking aggregation.
Meta Search Engine: A simple meta search engine with a new aggregation method. It supports customization of individual search engines to query.
Meta Recognition: Recognizes the meta blocks (title, authors, keywords, abstracts and references) in a research paper.
Deep Traffic: A course project to train a reinforcement learning model to drive a car on high traffic roads.
Learning to Run: An official challenge in the NIPS 2017 Competition Track. Developing a controller to enable a physiologically-based human model to navigate a complex obstacle course as quickly as possible.