Papers
Forecastable Component Analysis
Georg Goerg
Gated Autoencoders with Tied Input Weights
Droniou Alain, Sigaud Olivier
Gaussian Process Kernels for Pattern Discovery and Extrapolation
Andrew Wilson, Ryan Adams
Gaussian Process Vine Copulas for Multivariate Dependence
David Lopez-Paz, Jose Miguel Hernández-Lobato, Ghahramani Zoubin
General Functional Matrix Factorization Using Gradient Boosting
Tianqi Chen, Hang Li, Qiang Yang et al.
Generic Exploration and K-armed Voting Bandits
Tanguy Urvoy, Fabrice Clerot, Raphael Féraud et al.
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms
Jun Zhu, Ning Chen, Hugh Perkins et al.
Gossip-based distributed stochastic bandit algorithms
Balazs Szorenyi, Robert Busa-Fekete, Istvan Hegedus et al.
Guaranteed Sparse Recovery under Linear Transformation
Ji Liu, Lei Yuan, Jieping Ye
Guided Policy Search
Sergey Levine, Vladlen Koltun
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Jan-Willem Meent, Jonathan Bronson, Frank Wood et al.
Hierarchical Regularization Cascade for Joint Learning
Alon Zweig, Daphna Weinshall
Hierarchical Tensor Decomposition of Latent Tree Graphical Models
Le Song, Mariya Ishteva, Ankur Parikh et al.
Human Boosting
Harsh Pareek, Pradeep Ravikumar
Inference algorithms for pattern-based CRFs on sequence data
Rustem Takhanov, Vladimir Kolmogorov
Infinite Markov-Switching Maximum Entropy Discrimination Machines
Sotirios Chatzis
Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals
Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi et al.
Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines
Kohei Ogawa, Motoki Imamura, Ichiro Takeuchi et al.
Intersecting singularities for multi-structured estimation
Emile Richard, Francis BACH, Jean-Philippe Vert
Iterative Learning and Denoising in Convolutional Neural Associative Memories
Amin Karbasi, Amir Hesam Salavati, Amin Shokrollahi
Joint Transfer and Batch-mode Active Learning
Rita Chattopadhyay, Wei Fan, Ian Davidson et al.
Kernelized Bayesian Matrix Factorization
Mehmet Gönen, Suleiman Khan, Samuel Kaski
Label Partitioning For Sublinear Ranking
Jason Weston, Ameesh Makadia, Hector Yee
Large-Scale Bandit Problems and KWIK Learning
Jacob Abernethy, Kareem Amin, Michael Kearns et al.
Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, Brendan McMahan et al.