Papers
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data
Xiuwen Yi, Yu Zheng, Junbo Zhang et al.
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 Gradient Geodesic MCMC Methods
Chang Liu, Jun Zhu, Yang Song
Stochastic Gradient MCMC with Stale Gradients
Changyou Chen, Nan Ding, Chunyuan Li et al.
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Hongseok Namkoong, John C. Duchi
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Alain Durmus, Umut Simsekli, Eric Moulines et al.
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Stefan Lee, Senthil Purushwalkam Shiva Prakash, Michael Cogswell et al.
Stochastic Multiresolution Persistent Homology Kernel
Xiaojin Zhu, Ara Vartanian, Manish Bansal et al.
Stochastic Neural Networks with Monotonic Activation Functions
Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider et al.
Stochastic Online AUC Maximization
Yiming Ying, Longyin Wen, Siwei Lyu
Stochastic Optimization for Large-scale Optimal Transport
Aude Genevay, Marco Cuturi, Gabriel Peyré et al.
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares
Raman Arora, Poorya Mianjy, Teodor Marinov
Stochastic Planning in Large Search Spaces
Bilal Kartal
Stochastic Quasi-Newton Langevin Monte Carlo
Umut Simsekli, Roland Badeau, Taylan Cemgil et al.
Stochastic Structured Prediction under Bandit Feedback
Artem Sokolov, Julia Kreutzer, Stefan Riezler et al.
Stochastic Three-Composite Convex Minimization
Alp Yurtsever, Bang Cong Vu, Volkan Cevher
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.
Stochastic Variance Reduction Methods for Saddle-Point Problems
Balamurugan Palaniappan, Francis Bach
Stochastic Variational Deep Kernel Learning
Andrew G Wilson, Zhiting Hu, Ruslan Salakhutdinov et al.
Stochastic Variational Inference for the HDP-HMM
Aonan Zhang, San Gultekin, John Paisley
STON: Efficient Subtitling in Dutch Using State-of-the-Art Tools
Lyan Verwimp, Brecht Desplanques, Kris Demuynck et al.