Papers
Learning Efficient Markov Networks
Vibhav Gogate, William Webb, Pedro Domingos
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham Kakade, Ohad Shamir, Karthik Sindharan et al.
Learning from Candidate Labeling Sets
Jie Luo, Francesco Orabona
Learning From Crowds
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao et al.
Learning from Logged Implicit Exploration Data
Alex Strehl, John Langford, Lihong Li et al.
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
Qiang Wu, Justin Guinney, Mauro Maggioni et al.
Learning Instance-Specific Predictive Models
Shyam Visweswaran, Gregory F. Cooper
Learning invariant features using the Transformed Indian Buffet Process
Joseph L. Austerweil, Thomas L. Griffiths
Learning Kernels with Radiuses of Minimum Enclosing Balls
Kun Gai, Guangyun Chen, Chang-shui Zhang
Learning Multiple Tasks using Manifold Regularization
Arvind Agarwal, Samuel Gerber, Hal Daume
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty
Yi Zhang, Jeff G. Schneider
Learning Networks of Stochastic Differential Equations
José Pereira, Morteza Ibrahimi, Andrea Montanari
Learning Nonlinear Dynamic Models from Non-sequenced Data
Tzu–Kuo Huang, Le Song, Jeff Schneider
Learning Non-Stationary Dynamic Bayesian Networks
Joshua W. Robinson, Alexander J. Hartemink
Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
Learning Polyhedral Classifiers Using Logistic Function
Naresh Manwani, P. S. Sastry
Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors
Alessandro Chiuso, Gianluigi Pillonetto
Learning the context of a category
Dan Navarro
Learning the Structure of Deep Sparse Graphical Models
Ryan P. Adams, Hanna Wallach, Zoubin Ghahramani
Learning to combine foveal glimpses with a third-order Boltzmann machine
Hugo Larochelle, Geoffrey E. Hinton
Learning To Count Objects in Images
Victor Lempitsky, Andrew Zisserman
Learning to localise sounds with spiking neural networks
Dan Goodman, Romain Brette
Learning Translation Invariant Kernels for Classification
Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi
Learning via Gaussian Herding
Koby Crammer, Daniel D. Lee
Learning with Blocks: Composite Likelihood and Contrastive Divergence
Arthur Asuncion, Qiang Liu, Alexander Ihler et al.