Statistical Learning
Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
Andrew Spielberg, Allan Zhao, Yuanming Hu, Tao Du, Wojciech Matusik, Daniela Rus
Neural Information Processing Systems http://nips.cc/
42c8938e4cf5777700700e642dc2a8cd-AuthorFeedback.pdf
It also makes no assumptions on the sparseness of the transitions. Our experiments reflect this as well,25 as the transition probabilities are drawn from a uniform distribution with no sparseness assumptions and would be26 more difficult tothan sparse cases.
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Fan Zhou, Tengfei Li, Haibo Zhou, Hongtu Zhu, Ye Jieping
Graph-based semi-supervised learning isvery important for manyclassification tasks, but most existing methods assume that all labelled nodes are randomly sampled.
Provably robust boosted decision stumps and trees against adversarial attacks
Maksym Andriushchenko, Matthias Hein
Neural Information Processing Systems http://nips.cc/
