As such, we prevent policies from merely exploiting heuristic rewards without improving the task reward.

Manifold clustering is an important problem in motion and video segmentation, natural image clustering, and other applications where high-dimensional data lie on multiple, low-dimensional, nonlinear manifolds.

Second, we show the first core-set w.r.t. the sum-of-nearest-neighbor distances. Finally, we run several experiments showing the effectiveness of our core-set approach. In particular, we apply constrained diversity maximization to summarize a set of timed messages that takes into account the messages' recency.

We consider the problem of learning models for risk-sensitive reinforcement learning.

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In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints.

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.