Papers
Sound Abstraction and Decomposition of Probabilistic Programs
Steven Holtzen, Guy Broeck, Todd Millstein
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae, Andre Martins, Mathieu Blondel et al.
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Jeremias Knoblauch, Theodoros Damoulas
Spectrally Approximating Large Graphs with Smaller Graphs
Andreas Loukas, Pierre Vandergheynst
Spline Filters For End-to-End Deep Learning
Randall Balestriero, Romain Cosentino, Herve Glotin et al.
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Yuanxiang Gao, Li Chen, Baochun Li
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran, Ohad Shamir
SQL-Rank: A Listwise Approach to Collaborative Ranking
Liwei Wu, Cho-Jui Hsieh, James Sharpnack
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Zachary Charles, Dimitris Papailiopoulos
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
Jiong Zhang, Qi Lei, Inderjit Dhillon
Stagewise Safe Bayesian Optimization with Gaussian Processes
Yanan Sui, Vincent Zhuang, Joel Burdick et al.
State Abstractions for Lifelong Reinforcement Learning
David Abel, Dilip Arumugam, Lucas Lehnert et al.
State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch, Arno Solin, Alexander Grigorevskiy
Stein Points
Wilson Ye Chen, Lester Mackey, Jackson Gorham et al.
Stein Variational Gradient Descent Without Gradient
Jun Han, Qiang Liu
Stein Variational Message Passing for Continuous Graphical Models
Dilin Wang, Zhe Zeng, Qiang Liu
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Poorya Mianjy, Raman Arora
Stochastic Proximal Algorithms for AUC Maximization
Michael Natole, Yiming Ying, Siwei Lyu
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Jianfei Chen, Jun Zhu, Le Song
Stochastic Variance-Reduced Cubic Regularized Newton Methods
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Variance-Reduced Policy Gradient
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco et al.
Stochastic Video Generation with a Learned Prior
Emily Denton, Rob Fergus
Stochastic Wasserstein Barycenters
Sebastian Claici, Edward Chien, Justin Solomon
StrassenNets: Deep Learning with a Multiplication Budget
Michael Tschannen, Aran Khanna, Animashree Anandkumar