Event datasets involve irregular occurrences of events over the timeline and are prevalent in numerous domains.

We are also able to establish the accelerated linear rate for a function which is both strongly-convex and smooth.

The majority of algorithms involve building an explicit map during an exploration phase and then planning and acting via that representation. In this work, we are interested in pushing the limits of end-to-end deep reinforcement learning for navigation by proposing new methods and demonstrating their performance in large-scale, real-world environments.

Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's

Neural Information Processing Systems http://nips.cc/

In many real-world learning tasks, it is hard to directly optimize the true performance measures, meanwhile choosing the right surrogate objectives is also difficult. Under this situation, it is desirable to incorporate an optimization of objective process into the learning loop based on weak modeling of the relationship between the true measure and the objective. In this work, we discuss the task of objective adaptation, in which the learner iteratively adapts the learning objective to the underlying true objective based on the preference feedback from an oracle. We show that when the objective can be linearly parameterized, this preference based learning problem can be solved by utilizing the dueling bandit model.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

A contextual bandit is an online learning framework for modeling sequential decision-making problems.