Papers
Stochastic algorithms for entropy-regularized optimal transport problems
Brahim Khalil Abid, Robert Gower
Stochastic Answer Networks for Machine Reading Comprehension
Xiaodong Liu, Yelong Shen, Kevin Duh et al.
Stochastic Anytime Search for Bounding Marginal MAP
Radu Marinescu, Rina Dechter, Alexander Ihler
Stochastic Chebyshev Gradient Descent for Spectral Optimization
Insu Han, Haim Avron, Jinwoo Shin
Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
Yunwen Lei, Ke Tang
Stochastic Cubic Regularization for Fast Nonconvex Optimization
Nilesh Tripuraneni, Mitchell Stern, Chi Jin et al.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Jason Kuen, Xiangfei Kong, Zhe Lin et al.
Stochastic Expectation Maximization with Variance Reduction
Jianfei Chen, Jun Zhu, Yee Whye Teh et al.
Stochastic Fractional Hamiltonian Monte Carlo
Nanyang Ye, Zhanxing Zhu
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Pratik Chaudhari, Stefano Soatto
Stochastic Multi-armed Bandits in Constant Space
David Liau, Zhao Song, Eric Price et al.
Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu, Quanquan Gu
Stochastic Nonparametric Event-Tensor Decomposition
Shandian Zhe, Yishuai Du
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Poorya Mianjy, Raman Arora
Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
Conghui Tan, Tong Zhang, Shiqian Ma et al.
Stochastic Proximal Algorithms for AUC Maximization
Michael Natole, Yiming Ying, Siwei Lyu
Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models
Hongchang Gao, Heng Huang
Stochastic Shake-Shake Regularization for Affective Learning from Speech
Che-Wei Huang, Shrikanth Narayanan
Stochastic Spectral and Conjugate Descent Methods
Dmitry Kovalev, Peter Richtarik, Eduard Gorbunov et al.
Stochastic Three-Composite Convex Minimization with a Linear Operator
Renbo Zhao, Volkan Cevher
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 Variational Inference With Gradient Linearization
Tobias Plötz, Anne S. Wannenwetsch, Stefan Roth