Papers
Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
Benjamin Rolfs, Bala Rajaratnam, Dominique Guillot et al.
Joint Modeling of a Matrix with Associated Text via Latent Binary Features
Xianxing Zhang, Lawrence Carin
Kernel Hyperalignment
Alexander Lorbert, Peter J. Ramadge
Kernel Latent SVM for Visual Recognition
Weilong Yang, Yang Wang, Arash Vahdat et al.
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models
Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman et al.
Large Scale Distributed Deep Networks
Jeffrey Dean, Greg Corrado, Rajat Monga et al.
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning
Matthew Der, Lawrence K. Saul
Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs
Anima Anandkumar, Ragupathyraj Valluvan
Learned Prioritization for Trading Off Accuracy and Speed
Jiarong Jiang, Adam Teichert, Jason Eisner et al.
Learning about Canonical Views from Internet Image Collections
Elad Mezuman, Yair Weiss
Learning as MAP Inference in Discrete Graphical Models
Xianghang Liu, James Petterson, Tibério S. Caetano
Learning curves for multi-task Gaussian process regression
Peter Sollich, Simon Ashton
Learning from Distributions via Support Measure Machines
Krikamol Muandet, Kenji Fukumizu, Francesco Dinuzzo et al.
Learning from the Wisdom of Crowds by Minimax Entropy
Dengyong Zhou, Sumit Basu, Yi Mao et al.
Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs
Aharon Birnbaum, Shai S. Shwartz
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
Assaf Glazer, Michael Lindenbaum, Shaul Markovitch
Learning Image Descriptors with the Boosting-Trick
Tomasz Trzcinski, Mario Christoudias, Vincent Lepetit et al.
Learning Invariant Representations of Molecules for Atomization Energy Prediction
Grégoire Montavon, Katja Hansen, Siamac Fazli et al.
Learning Label Trees for Probabilistic Modelling of Implicit Feedback
Andriy Mnih, Yee W. Teh
Learning Manifolds with K-Means and K-Flats
Guillermo Canas, Tomaso Poggio, Lorenzo Rosasco
Learning Mixtures of Tree Graphical Models
Anima Anandkumar, Daniel J. Hsu, Furong Huang et al.
Learning Multiple Tasks using Shared Hypotheses
Koby Crammer, Yishay Mansour
Learning Networks of Heterogeneous Influence
Nan Du, Le Song, Ming Yuan et al.
Learning optimal spike-based representations
Ralph Bourdoukan, David Barrett, Sophie Deneve et al.