We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We first show that any c...
Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
d Abstract] Dana Angluin James Aspnes Jiang Chen Yinghua Wu We propose a new model for exact learning of acyclic circuits using experiments in which chosen values may be assigned ...
Dana Angluin, James Aspnes, Jiang Chen, Yinghua Wu
This paper proposes an approach to mixed environment training of manual tasks requiring concurrent use of psychomotor and cognitive skills. To train concurrent use of both skill s...
Aaron Kotranza, D. Scott Lind, Carla M. Pugh, Benj...