Papers
Learning the Structure of Deep Sparse Graphical Models
Ryan P. Adams, Hanna Wallach, Zoubin Ghahramani
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Arthur Asuncion, Qiang Liu, Alexander Ihler et al.
Locally Linear Denoising on Image Manifolds
Dian Gong, Fei Sha, Gérard Medioni
Matrix-Variate Dirichlet Process Mixture Models
Zhihua Zhang, Guang Dai, Michael I. Jordan
Maximum-likelihood learning of cumulative distribution functions on graphs
Jim Huang, Nebojsa Jojic
Model-Free Monte Carlo-like Policy Evaluation
Raphael Fonteneau, Susan Murphy, Louis Wehenkel et al.
Modeling annotator expertise: Learning when everybody knows a bit of something
Yan Yan, Romer Rosales, Glenn Fung et al.
Multiclass-Multilabel Classification with More Classes than Examples
Ofer Dekel, Ohad Shamir
Multitask Learning for Brain-Computer Interfaces
Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun
Multi-Task Learning using Generalized t Process
Yu Zhang, Dit–Yan Yeung
Near-Optimal Evasion of Convex-Inducing Classifiers
Blaine Nelson, Benjamin Rubinstein, Ling Huang et al.
Negative Results for Active Learning with Convex Losses
Steve Hanneke, Liu Yang
Neural conditional random fields
Trinh–Minh–Tri Do, Thierry Artieres
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann, Aapo Hyvärinen
Nonlinear functional regression: a functional RKHS approach
Hachem Kadri, Emmanuel Duflos, Philippe Preux et al.
Nonparametric Bayesian Matrix Factorization by Power-EP
Nan Ding, Yuan Qi, Rongjing Xiang et al.
Nonparametric prior for adaptive sparsity
Vikas Raykar, Linda Zhao
Nonparametric Tree Graphical Models
Le Song, Arthur Gretton, Carlos Guestrin
On Combining Graph-based Variance Reduction schemes
Vibhav Gogate, Rina Dechter
Online Anomaly Detection under Adversarial Impact
Marius Kloft, Pavel Laskov
Online Passive-Aggressive Algorithms on a Budget
Zhuang Wang, Slobodan Vucetic
On the Convergence Properties of Contrastive Divergence
Ilya Sutskever, Tijmen Tieleman
On the Impact of Kernel Approximation on Learning Accuracy
Corinna Cortes, Mehryar Mohri, Ameet Talwalkar
On the relation between universality, characteristic kernels and RKHS embedding of measures
Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet