Papers
Starting Small - Learning with Adaptive Sample Sizes
Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
Nihar Shah, Sivaraman Balakrishnan, Aditya Guntuboyina et al.
Stochastic Block BFGS: Squeezing More Curvature out of Data
Robert Gower, Donald Goldfarb, Peter Richtarik
Stochastic Discrete Clenshaw-Curtis Quadrature
Nico Piatkowski, Katharina Morik
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Raman Arora, Poorya Mianjy, Teodor Marinov
Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil et al.
Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning
Xingguo Li, Tuo Zhao, Raman Arora et al.
Stochastic Variance Reduction for Nonconvex Optimization
Sashank J. Reddi, Ahmed Hefny, Suvrit Sra et al.
Stratified Sampling Meets Machine Learning
Edo Liberty, Kevin Lang, Konstantin Shmakov
Strongly-Typed Recurrent Neural Networks
David Balduzzi, Muhammad Ghifary
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Christos Louizos, Max Welling
Structured Prediction Energy Networks
David Belanger, Andrew McCallum
Structure Learning of Partitioned Markov Networks
Song Liu, Taiji Suzuki, Masashi Sugiyama et al.
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
Rie Johnson, Tong Zhang
Tensor Decomposition via Joint Matrix Schur Decomposition
Nicolo Colombo, Nikos Vlassis
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Dmitry Ulyanov, Vadim Lebedev, Andrea et al.
The Arrow of Time in Multivariate Time Series
Stefan Bauer, Bernhard Schölkopf, Jonas Peters
The Information Sieve
Greg Ver Steeg, Aram Galstyan
The Information-Theoretic Requirements of Subspace Clustering with Missing Data
Daniel Pimentel-Alarcon, Robert Nowak
The knockoff filter for FDR control in group-sparse and multitask regression
Ran Dai, Rina Barber
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks
Yingfei Wang, Chu Wang, Warren Powell
The Label Complexity of Mixed-Initiative Classifier Training
Jina Suh, Xiaojin Zhu, Saleema Amershi
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson et al.
The Sum-Product Theorem: A Foundation for Learning Tractable Models
Abram Friesen, Pedro Domingos
The Teaching Dimension of Linear Learners
Ji Liu, Xiaojin Zhu, Hrag Ohannessian