Papers
Sequential Random Sampling Revisited: Hidden Shuffle Method
Michael Shekelyan, Graham Cormode
SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation
Robert Gower, Othmane Sebbouh, Nicolas Loizou
Shadow Manifold Hamiltonian Monte Carlo
Chris van der Heide, Fred Roosta, Liam Hodgkinson et al.
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
Jiaxuan Wang, Jenna Wiens, Scott Lundberg
Sharp Analysis of a Simple Model for Random Forests
Jason Klusowski
Shuffled Model of Differential Privacy in Federated Learning
Antonious Girgis, Deepesh Data, Suhas Diggavi et al.
Significance of Gradient Information in Bayesian Optimization
Shubhanshu Shekhar, Tara Javidi
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Xing Han, Sambarta Dasgupta, Joydeep Ghosh
Sketch based Memory for Neural Networks
Rina Panigrahy, Xin Wang, Manzil Zaheer
Smooth Bandit Optimization: Generalization to Holder Space
Yusha Liu, Yining Wang, Aarti Singh
SONIA: A Symmetric Blockwise Truncated Optimization Algorithm
Majid Jahani, MohammadReza Nazari, Rachael Tappenden et al.
Sparse Algorithms for Markovian Gaussian Processes
William Wilkinson, Arno Solin, Vincent Adam
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi, Markus Heinonen, Edwin Bonilla et al.
Spectral Tensor Train Parameterization of Deep Learning Layers
Anton Obukhov, Maxim Rakhuba, Alexander Liniger et al.
Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss
Zhenhuan Yang, Yunwen Lei, Siwei Lyu et al.
Stability and Risk Bounds of Iterative Hard Thresholding
Xiaotong Yuan, Ping Li
Stable ResNet
Soufiane Hayou, Eugenio Clerico, Bobby He et al.
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference
Sean Plummer, Shuang Zhou, Anirban Bhattacharya et al.
Stochastic Bandits with Linear Constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett et al.
Stochastic Linear Bandits Robust to Adversarial Attacks
Ilija Bogunovic, Arpan Losalka, Andreas Krause et al.
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji et al.
Taming heavy-tailed features by shrinkage
Ziwei Zhu, Wenjing Zhou
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun Ding, Ziyang Wu et al.
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller, Guillaume Rabusseau, John Terilla