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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Variance Reduction for Faster Non-Convex Optimization ICML 2016
Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016
Learning Parameters of Hybrid Time Bayesian Networks PGM 2016
Black-Box Alpha Divergence Minimization ICML 2016
Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016
Improved Dropout for Shallow and Deep Learning NIPS 2016
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent NIPS 2016
Stochastic Variance Reduction Methods for Saddle-Point Problems NIPS 2016
Towards Stability and Optimality in Stochastic Gradient Descent AISTATS 2016
Slice Sampling on Hamiltonian Trajectories ICML 2016
Variance-Reduced and Projection-Free Stochastic Optimization ICML 2016
Scaling-up Empirical Risk Minimization: Optimization of Incomplete $U$-statistics JMLR 2016
Improving Structure MCMC for Bayesian Networks through Markov Blanket Resampling JMLR 2016
Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels JMLR 2016
Adaptive Sampling for SGD by Exploiting Side Information ICML 2016
Optimal Learning for Multi-pass Stochastic Gradient Methods NIPS 2016
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes NIPS 2016
Online and Differentially-Private Tensor Decomposition NIPS 2016
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back NIPS 2016
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares NIPS 2016
Without-Replacement Sampling for Stochastic Gradient Methods NIPS 2016
Stochastic Optimization for Multiview Representation Learning using Partial Least Squares ICML 2016
Variance Reduction in Stochastic Gradient Langevin Dynamics NIPS 2016
Cyclades: Conflict-free Asynchronous Machine Learning NIPS 2016
Coin Betting and Parameter-Free Online Learning NIPS 2016