Abstract: Recent years have seen a surging interest in developing under-approximations of reachable sets due to their potential applications in control synthesis and verification. In this letter, we ...
Abstract: We consider the problem of online sparse linear approximation, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in ...
One challenge in large scale data science is that even linear algorithms can result in large data processing cost and long latency, which limit the interactivity of the system and the productivity of ...
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