Dr. Frongillo's research interests lie in the interface of theoretical machine learning and algorithmic economics, encompassing topics such as the design of loss functions in machine learning, and incentive-compatible mechanisms to elicit information from individuals or crowds. He is also active in the emerging area of game-theoretic statistics. Broad questions describing his current focus include the following: How can we systematically design loss functions for challenging machine learning problems like structured prediction? How can we design better incentives in machine learning and forecasting?
keywords
algorithmic economics, theoretical machine learning, information elicitation, game-theoretic statistics
Publications
selected publications
chapter
Parallel Boosting with Momentum.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
17-32.
2013
Elicitation for Aggregation.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.
900-906.
2015
Social Learning in a Changing World.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
146-157.
2011
On Learning Algorithms for Nash Equilibria.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
114-+.
2010
