Papers
Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations
Timothy Mann, Shie Mannor
Scaling Up Robust MDPs using Function Approximation
Aviv Tamar, Shie Mannor, Huan Xu
Signal recovery from Pooling Representations
Joan Bruna Estrach, Arthur Szlam, Yann LeCun
Skip Context Tree Switching
Marc Bellemare, Joel Veness, Erik Talvitie
Sparse meta-Gaussian information bottleneck
Melani Rey, Volker Roth, Thomas Fuchs
Sparse Reinforcement Learning via Convex Optimization
Zhiwei Qin, Weichang Li, Firdaus Janoos
Spectral Bandits for Smooth Graph Functions
Michal Valko, Remi Munos, Branislav Kveton et al.
Spectral Regularization for Max-Margin Sequence Tagging
Ariadna Quattoni, Borja Balle, Xavier Carreras et al.
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions
Shiwei Lan, Bo Zhou, Babak Shahbaba
Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery
Cun Mu, Bo Huang, John Wright et al.
Stable and Efficient Representation Learning with Nonnegativity Constraints
Tsung-Han Lin, H. T. Kung
Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance
Simone Romano, James Bailey, Vinh Nguyen et al.
Statistical analysis of stochastic gradient methods for generalized linear models
Panagiotis Toulis, Edoardo Airoldi, Jason Rennie
Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional Setting
Yudong Chen, Jiaming Xu
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily Fox, Carlos Guestrin
Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices
Jose Miguel Hernandez-Lobato, Neil Houlsby, Zoubin Ghahramani
Stochastic Neighbor Compression
Matt Kusner, Stephen Tyree, Kilian Weinberger et al.
Stochastic Variational Inference for Bayesian Time Series Models
Matthew Johnson, Alan Willsky
Structured Generative Models of Natural Source Code
Chris Maddison, Daniel Tarlow
Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing
Benjamin Haeffele, Eric Young, Rene Vidal
Structured Prediction of Network Response
Hongyu Su, Aristides Gionis, Juho Rousu
Structured Recurrent Temporal Restricted Boltzmann Machines
Roni Mittelman, Benjamin Kuipers, Silvio Savarese et al.
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale et al.