Papers
Bayesian Gaussian Process Latent Variable Model
Michalis Titsias, Neil D. Lawrence
Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
Ivan Titov, Alexandre Klementiev, Kevin Small et al.
Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
Hartigan’s Method: k-means Clustering without Voronoi
Matus Telgarsky, Andrea Vattani
HOP-MAP: Efficient Message Passing with High Order Potentials
Daniel Tarlow, Inmar Givoni, Richard Zemel
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
Taiji Suzuki, Masashi Sugiyama
Inference and Learning in Networks of Queues
Charles Sutton, Michael I. Jordan
On the Convergence Properties of Contrastive Divergence
Ilya Sutskever, Tijmen Tieleman
Conditional Density Estimation via Least-Squares Density Ratio Estimation
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki et al.
On the relation between universality, characteristic kernels and RKHS embedding of measures
Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet
Nonparametric Tree Graphical Models
Le Song, Arthur Gretton, Carlos Guestrin
Detecting Weak but Hierarchically-Structured Patterns in Networks
Aarti Singh, Robert Nowak, Robert Calderbank
Reduced-Rank Hidden Markov Models
Sajid Siddiqi, Byron Boots, Geoffrey Gordon
Empirical Bernstein Boosting
Pannagadatta Shivaswamy, Tony Jebara
Dense Message Passing for Sparse Principal Component Analysis
Kevin Sharp, Magnus Rattray
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
Mark Schmidt, Kevin Murphy
Factorized Orthogonal Latent Spaces
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun et al.
Efficient Learning of Deep Boltzmann Machines
Ruslan Salakhutdinov, Hugo Larochelle
Reducing Label Complexity by Learning From Bags
Sivan Sabato, Nathan Srebro, Naftali Tishby
Active Sequential Learning with Tactile Feedback
Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar
Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor, Manfred Opper
Efficient Reductions for Imitation Learning
Stephane Ross, Drew Bagnell
A Regularization Approach to Nonlinear Variable Selection
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci et al.