Papers
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin Duke, Piyush Rai
Stochastic Deep Networks
Gwendoline De Bie, Gabriel Peyré, Marco Cuturi
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas et al.
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Baojian Zhou, Feng Chen, Yiming Ying
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin et al.
Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch et al.
Sublinear quantum algorithms for training linear and kernel-based classifiers
Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space
Yifan Lei, Qiang Huang, Mohan Kankanhalli et al.
Submodular Cost Submodular Cover with an Approximate Oracle
Victoria Crawford, Alan Kuhnle, My Thai
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
Chris Harshaw, Moran Feldman, Justin Ward et al.
Submodular Observation Selection and Information Gathering for Quadratic Models
Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo et al.
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity
Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam et al.
Subspace Robust Wasserstein Distances
François-Pierre Paty, Marco Cuturi
Sum-of-Squares Polynomial Flow
Priyank Jaini, Kira A. Selby, Yaoliang Yu
Supervised Hierarchical Clustering with Exponential Linkage
Nishant Yadav, Ari Kobren, Nicholas Monath et al.
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization
Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona
SWALP : Stochastic Weight Averaging in Low Precision Training
Guandao Yang, Tianyi Zhang, Polina Kirichenko et al.
Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck, Jan Peters, Patrick Van Der Smagt
Taming MAML: Efficient unbiased meta-reinforcement learning
Hao Liu, Richard Socher, Caiming Xiong
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
Sung Whan Yoon, Jun Seo, Jaekyun Moon
Target-Based Temporal-Difference Learning
Donghwan Lee, Niao He
Target Tracking for Contextual Bandits: Application to Demand Side Management
Margaux Brégère, Pierre Gaillard, Yannig Goude et al.
TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff et al.
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning
Yilun Du, Karthic Narasimhan