Managing large-scale systems often involves simultaneously solving thousands of unrelated stochastic optimization problems, each with limited data. Intuition suggests that one can decouple these problems and solve them separately without loss generality. We propose a novel data-pooling algorithm called Shrunken-SAA disproves this intuition. In particular, we prove combining data across outperfo...

The task of learning from point cloud data is always challenging due to the often occurrence noise and outliers in data. Such inaccuracies can significantly influence performance state-of-the-art deep networks their ability classify or segment objects. While there are some robust approaches, they computationally too expensive for real-time applications. This paper proposes a solution that inclu...