Papers
8,340 papers found
Slice Sampling on Hamiltonian Trajectories
Benjamin Bloem-Reddy, John Cunningham
Smooth Imitation Learning for Online Sequence Prediction
Hoang Le, Andrew Kang, Yisong Yue et al.
Softened Approximate Policy Iteration for Markov Games
Julien Pérolat, Bilal Piot, Matthieu Geist et al.
Solving Ridge Regression using Sketched Preconditioned SVRG
Alon Gonen, Francesco Orabona, Shai Shalev-Shwartz
Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity
Zhuoran Yang, Zhaoran Wang, Han Liu et al.
Sparse Parameter Recovery from Aggregated Data
Avradeep Bhowmik, Joydeep Ghosh, Oluwasanmi Koyejo
Speeding up k-means by approximating Euclidean distances via block vectors
Thomas Bottesch, Thomas Bühler, Markus Kächele
Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies
David Inouye, Pradeep Ravikumar, Inderjit Dhillon
Stability of Controllers for Gaussian Process Forward Models
Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong et al.
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.