To start from scratch, we introduce a new language learning setting relevant to building adaptive natural language interfaces.
We focus on a game-theoretic regression learning setting where private information is elicited from multiple agents with different, possibly conflicting, views on how to label the points of an input space.
Interactive learning focuses on enhancing an existing machine-learning model based on newly acquired information, which is not possible in a classical machine learning setting.
By analyzing the settings of the primary research, a set of five moderators was identified: grade level, instrumentation, treatment duration, content domain, and type of learning setting.
The objective of the study was to determine whether the undergraduate medical students' course that is taught traditionally (in a face to face setting), could also be taught in a complementary distance learning setting.
In this paper, we introduce this novel stochastic adversarial learning setting and we analyze its learnability.
In the reinforcement learning setting, gradients of the policy can still be obtained using the likelihood ratio method.
This paper uses one of the four experiments, spectroscopy for introductory science in a distance learning setting, to illustrate the process of developing remote experiments.
We think that interactivity is important, and that an interactive language learning setting will enable adaptive and customizable systems, especially for resource-poor languages and new domains where starting from close to scratch is unavoidable.
Finally, in the unsupervised transfer learning setting, the target task is different from but related to the source task.
