Statistical Learning
On model selection consistency of penalized M-estimators: a geometric theory
Jason D. Lee, Yuekai Sun, Jonathan E. Taylor
Neural Information Processing Systems http://nips.cc/
Conditional Generative Moment-Matching Networks
Yong Ren, Jun Zhu, Jialian Li, Yucen Luo
Neural Information Processing Systems http://nips.cc/
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
Neural Information Processing Systems http://nips.cc/
A fast, universal algorithm to learn parametric nonlinear embeddings
Miguel A. Carreira-Perpinan, Max Vladymyrov
Using the method of auxiliary coordinates, we derive a training algorithm that works by alternating steps that train an auxiliary embeddingwith steps that train the mapping.
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections
Matthew Lawlor, Steven W. Zucker
Neural Information Processing Systems http://nips.cc/
Tagger: Deep Unsupervised Perceptual Grouping
Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hao, Harri Valpola, Jรผrgen Schmidhuber
Neural Information Processing Systems http://nips.cc/
A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars K. Hansen, Sรธren Hauberg
The underlyingmetricis,however,non-parametric.Wedevelopamaximumlikelihood algorithm to infer the distribution parameters that relies on a combination of gradient descent and Monte Carlo integration. We further extend the LAND to mixture models, andprovidethecorresponding EMalgorithm.
Approximate Gaussian process inference for the drift function in stochastic differential equations
Andreas Ruttor, Philipp Batz, Manfred Opper
Neural Information Processing Systems http://nips.cc/
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
Zheng Qu, Peter Richtarik, Tong Zhang
Neural Information Processing Systems http://nips.cc/