We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
Lihong Li, Michael L. Littman, Christopher R. Mans...
Online Social networks are increasingly being seen as a means of obtaining awareness of user preferences. Such awareness could be used to target goods and services at them. We cons...
This paper builds upon action and design research aimed at enhancing scholarly community and conversation in a graduate school setting. In this paper we focus on knowledge sharing...
Brian Thoms, Nathan Garrett, Jesus Canelon Herrera...
: In this paper we present two computational approaches that can be used characterize and measure online threaded discussions and demonstrate that they can objectively validate stu...