Papers
Data Skeletonization via Reeb Graphs
Xiaoyin Ge, Issam I. Safa, Mikhail Belkin et al.
Deep Learners Benefit More from Out-of-Distribution Examples
Yoshua Bengio, Frédéric Bastien, Arnaud Bergeron et al.
Deep Learning for Efficient Discriminative Parsing
Ronan Collobert
Deep Sparse Rectifier Neural Networks
Xavier Glorot, Antoine Bordes, Yoshua Bengio
Demixed Principal Component Analysis
Wieland Brendel, Ranulfo Romo, Christian K. Machens
Dependent Hierarchical Beta Process for Image Interpolation and Denoising
Mingyuan Zhou, Hongxia Yang, Guillermo Sapiro et al.
Designing Petri Net Supervisors from LTL Specifications
Bruno Lacerda, Pedro Lima
Differentially Private Empirical Risk Minimization
Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate
Differentially Private M-Estimators
Jing Lei
Dimensionality Reduction for Spectral Clustering
Donglin Niu, Jennifer Dy, Michael I. Jordan
Dimensionality Reduction Using the Sparse Linear Model
Ioannis A. Gkioulekas, Todd Zickler
Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators
Dominique C. Perrault-joncas, Marina Meila
Directional Statistics on Permutations
Sergey M. Plis, Stephen McCracken, Terran Lane et al.
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa et al.
Dirichlet Process Mixtures of Generalized Linear Models
Lauren A. Hannah, David M. Blei, Warren B. Powell
Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira et al.
Discussion of “A conditional game for comparing approximations”
Vincent Conitzer
Discussion of “Spectral Dimensionality Reduction via Maximum Entropy”
Laurens van der Maaten
Distance Dependent Chinese Restaurant Processes
David M. Blei, Peter I. Frazier