Statistical Learning
Efficiently avoiding saddle points with zero order methods: No gradients required
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Neural Information Processing Systems http://nips.cc/
Precision-Recall-Gain Curves: PR Analysis Done Right
Neural Information Processing Systems http://nips.cc/
Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees
Alix LHERITIER, Frederic Cazals
Subsequently, it adapts its model in order to better predict future symbols.
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs.
Kullback-Leibler Proximal Variational Inference
Mohammad Emtiyaz E. Khan, Pierre Baque, Franรงois Fleuret, Pascal Fua
Neural Information Processing Systems http://nips.cc/
Ultrahyperbolic Representation Learning
Ultrahyperbolic Representation Learning
Ultrahyperbolic Representation Learning
It contains the entire family of hyperboloids as submanifolds.
Sinkhorn Natural Gradient for Generative Models
We consider the problem of minimizing a functional over a parametric family of probability measures, where the parameterization is characterized via a push-forward structure. An important application of this problem is in training generative adversarial networks.
