Papers
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
Priyank Jaini, Didrik Nielsen, Max Welling
Scalable Constrained Bayesian Optimization
David Eriksson, Matthias Poloczek
Scalable Gaussian Process Variational Autoencoders
Metod Jazbec, Matt Ashman, Vincent Fortuin et al.
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Hyun-Suk Lee, Cong Shen, William Zame et al.
Selective Classification via One-Sided Prediction
Aditya Gangrade, Anil Kag, Venkatesh Saligrama
Self-Concordant Analysis of Generalized Linear Bandits with Forgetting
Yoan Russac, Louis Faury, Olivier Cappé et al.
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry
Qadeer Khan, Patrick Wenzel, Daniel Cremers
Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees
Alessio Mazzetto, Dylan Sam, Andrew Park et al.
Semi-Supervised Learning with Meta-Gradient
Taihong Xiao, Xin-Yu Zhang, Haolin Jia et al.
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