Papers
Deep Learning using Robust Interdependent Codes
Hugo Larochelle, Dumitru Erhan, Pascal Vincent
Sampling Techniques for the Nystrom Method
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar
Convex Perturbations for Scalable Semidefinite Programming
Brian Kulis, Suvrit Sra, Inderjit Dhillon
Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Nicole Kramer, Masashi Sugiyama, Mikio Braun
Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings
Minyoung Kim, Vladimir Pavlovic
Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards
Varun Kanade, H. Brendan McMahan, Brent Bryan
Data Biased Robust Counter Strategies
Michael Johanson, Michael Bowling
Particle Belief Propagation
Alexander Ihler, David McAllester
Exploiting Probabilistic Independence for Permutations
Jonathan Huang, Carlos Guestrin, Xiaoye Jiang et al.
Maximum Entropy Density Estimation with Incomplete Presence-Only Data
Bert Huang, Ansaf Salleb-Aouissi
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward
Matthew Hoffman, Nando Freitas, Arnaud Doucet et al.
Infinite Hierarchical Hidden Markov Models
Katherine Heller, Yee Whye Teh, Dilan Gorur
Distilled sensing: selective sampling for sparse signal recovery
Jarvis Haupt, Rui Castro, Robert Nowak
Network Completion and Survey Sampling
Steve Hanneke, Eric P. Xing
Active Learning as Non-Convex Optimization
Andrew Guillory, Erick Chastain, Jeff Bilmes
Visualization Databases for the Analysis of Large Complex Datasets
Saptarshi Guha, Paul Kidwell, Ryan P. Hafen et al.
Sparse Probabilistic Principal Component Analysis
Yue Guan, Jennifer Dy
Residual Splash for Optimally Parallelizing Belief Propagation
Joseph Gonzalez, Yucheng Low, Carlos Guestrin
Multi-Manifold Semi-Supervised Learning
Andrew Goldberg, Xiaojin Zhu, Aarti Singh et al.
Semi-Supervised Affinity Propagation with Instance-Level Constraints
Inmar Givoni, Brendan Frey
The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training
Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio et al.
Choosing a Variable to Clamp
Frederik Eaton, Zoubin Ghahramani
Variational Inference for the Indian Buffet Process
Finale Doshi, Kurt Miller, Jurgen Van Gael et al.
Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Joshua Dillon, Guy Lebanon
Matching Pursuit Kernel Fisher Discriminant Analysis
Tom Diethe, Zakria Hussain, David Hardoon et al.