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Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
Stephen Mussmann, Percy S. Liang
Neural Information Processing Systems http://nips.cc/
Saccader: Improving Accuracy of Hard Attention Models for Vision
Gamaleldin Elsayed, Simon Kornblith, Quoc V. Le
Neural Information Processing Systems http://nips.cc/
Decentralized sketching of low rank matrices
Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg
A fundamental structural model for data is that the data points lie close to an unknown subspace, meaning that the matrix created by concatenating the data vectors has low rank. We address a particular low-rank matrix recovery problem where we wish to recover a set of vectors from a low-dimensional subspace after they have been individually compressed (or "sketched").
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, Jason D. Lee
This would intomeasuring may see Corollary .
f6ccfa588d2a95bef5a3b101c02524c9-Supplemental-Conference.pdf
It is known that Binary Segmentation is consistent but not optimal (Venkatraman (1992)). As an improvement, Fryzlewicz (2014) propose WildBinary Segmentation andshowthatithasabetter localization rate.
DetectingAbruptChangesinSequentialPairwise ComparisonData
In this paper we are concerned with localizing the change points in ahigh-dimensional BTL model with piecewiseconstantparameters.
