Papers
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin Duke, Piyush Rai
Stochastic Canonical Correlation Analysis
Chao Gao, Dan Garber, Nathan Srebro et al.
Stochastic Class-Based Hard Example Mining for Deep Metric Learning
Yumin Suh, Bohyung Han, Wonsik Kim et al.
Stochastic Constraint Propagation for Mining Probabilistic Networks
Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match
Amin Karbasi, Hamed Hassani, Aryan Mokhtari et al.
Stochastic Deep Networks
Gwendoline De Bie, Gabriel Peyré, Marco Cuturi
Stochastic Exposure Coding for Handling Multi-ToF-Camera Interference
Jongho Lee, Mohit Gupta
Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels
Felix J.S. Bragman, Ryutaro Tanno, Sebastien Ourselin et al.
Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions
Adrien Taylor, Francis Bach
Stochastic Frank-Wolfe for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever, Olivier Fercoq et al.
Stochastic Goal Recognition Design
Christabel Wayllace
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate
Mor Shpigel Nacson, Nathan Srebro, Daniel Soudry
Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors
Atsushi Nitanda, Taiji Suzuki
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou, Pan Xu, Quanquan Gu
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization
Navid Azizan, Babak Hassibi
Stochastic Gradient Push for Distributed Deep Learning
Mahmoud Assran, Nicolas Loizou, Nicolas Ballas et al.
Stochastic Gradient Trees
Henry Gouk, Bernhard Pfahringer, Eibe Frank
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Baojian Zhou, Feng Chen, Yiming Ying
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
Qianxiao Li, Cheng Tai, Weinan E
Stochastic Negative Mining for Learning with Large Output Spaces
Sashank J. Reddi, Satyen Kale, Felix Yu et al.
Stochastic Nonconvex Optimization with Large Minibatches
Weiran Wang, Nathan Srebro
Stochastic Optimal Control as Approximate Input Inference
Joe Watson, Hany Abdulsamad, Jan Peters
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence
Yi Xu, Qi Qi, Qihang Lin et al.
Stochastic Optimization of Sorting Networks via Continuous Relaxations
Aditya Grover, Eric Wang, Aaron Zweig et al.