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Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Embarrassingly Parallel MCMC using Deep Invertible Transformations UAI 2019
Incremental Randomized Sketching for Online Kernel Learning ICML 2019
Cogra: Concept-Drift-Aware Stochastic Gradient Descent for Time-Series Forecasting AAAI 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing AAAI 2019
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization ICML 2019
On Testing of Uniform Samplers AAAI 2019
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines AAAI 2019
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning AAAI 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019
Online High Rank Matrix Completion CVPR 2019
Continuous-time Models for Stochastic Optimization Algorithms NIPS 2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NIPS 2019
Tensor Monte Carlo: Particle Methods for the GPU era NIPS 2019
Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019
Towards closing the gap between the theory and practice of SVRG NIPS 2019
Stochastic Negative Mining for Learning with Large Output Spaces AISTATS 2019
On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019
Proximal Splitting Meets Variance Reduction AISTATS 2019
Pathwise Derivatives for Multivariate Distributions AISTATS 2019
Approximations of the Restless Bandit Problem JMLR 2019
Hamiltonian Monte Carlo with Energy Conserving Subsampling JMLR 2019
On the Convergence of Gaussian Belief Propagation with Nodes of Arbitrary Size JMLR 2019
Stochastic Canonical Correlation Analysis JMLR 2019
Online Variance Reduction with Mixtures ICML 2019