Papers
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
Steven Chase, Andrew Schwartz, Wolfgang Maass et al.
Gaussian process regression with Student-t likelihood
Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
Jing Gao, Feng Liang, Wei Fan et al.
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
Yusuke Watanabe, Kenji Fukumizu
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
Grzegorz Swirszcz, Naoki Abe, Aurelie C. Lozano
Group Sparse Coding
Samy Bengio, Fernando Pereira, Yoram Singer et al.
Heavy-Tailed Symmetric Stochastic Neighbor Embedding
Zhirong Yang, Irwin King, Zenglin Xu et al.
Help or Hinder: Bayesian Models of Social Goal Inference
Tomer Ullman, Chris Baker, Owen Macindoe et al.
Heterogeneous multitask learning with joint sparsity constraints
Xiaolin Yang, Seyoung Kim, Eric P. Xing
Hierarchical Learning of Dimensional Biases in Human Categorization
Adam Sanborn, Nick Chater, Katherine A. Heller
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions
Bangpeng Yao, Dirk Walther, Diane Beck et al.
Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions
Matthias Bethge, Eero P. Simoncelli, Fabian H. Sinz
Human Rademacher Complexity
Xiaojin Zhu, Bryan R. Gibson, Timothy T. Rogers
Improving Existing Fault Recovery Policies
Guy Shani, Christopher Meek
Indian Buffet Processes with Power-law Behavior
Yee W. Teh, Dilan Gorur
Individuation, Identification and Object Discovery
Charles Kemp, Alan Jern, Fei Xu
Information-theoretic lower bounds on the oracle complexity of convex optimization
Alekh Agarwal, Martin J. Wainwright, Peter L. Bartlett et al.
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
Miguel Lázaro-Gredilla, Aníbal Figueiras-Vidal
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions
Kenji Fukumizu, Arthur Gretton, Gert R. Lanckriet et al.
Kernel Methods for Deep Learning
Youngmin Cho, Lawrence K. Saul
Kernels and learning curves for Gaussian process regression on random graphs
Peter Sollich, Matthew Urry, Camille Coti
Know Thy Neighbour: A Normative Theory of Synaptic Depression
Jean-pascal Pfister, Peter Dayan, Máté Lengyel
Label Selection on Graphs
Andrew Guillory, Jeff A. Bilmes
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Finale Doshi-velez, Shakir Mohamed, Zoubin Ghahramani et al.