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stochastic gradient

296 papers

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Also known as

SGVB SVRG SGHMC SGLD SG SGMCMC SGD

Co-occurring keywords

variance reduction (520) markov chain monte carlo (724) stochastic optimization (1060) bayesian inference (1904) stochastic gradient descent (1088) convex optimization (1320) nonconvex optimization (316) convergence rate (606) langevin dynamics (128) variational inference (2069)

Papers

Learning with risk-averse feedback under potentially heavy tails AISTATS 2021
Finding First-Order Nash Equilibria of Zero-Sum Games with the Regularized Nikaido-Isoda Function AISTATS 2021
Fractional moment-preserving initialization schemes for training deep neural networks AISTATS 2021
On the Bias-Variance-Cost Tradeoff of Stochastic Optimization NIPS 2021
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Optimal Underdamped Langevin MCMC Method NIPS 2021
Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems NIPS 2021
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models AISTATS 2021
Distributed Saddle-Point Problems Under Data Similarity NIPS 2021
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NIPS 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks ICML 2020
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020
Risk Bounds for Low Cost Bipartite Ranking UAI 2020
StochasticRank: Global Optimization of Scale-Free Discrete Functions ICML 2020
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization with Nearly Optimal Generalization ICML 2020
An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning AAAI 2020
D-SPIDER-SFO: A Decentralized Optimization Algorithm with Faster Convergence Rate for Nonconvex Problems AAAI 2020
An Implicit Form of Krasulina's k-PCA Update without the Orthonormality Constraint AAAI 2020
Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization NIPS 2020
Projected Stein Variational Gradient Descent NIPS 2020
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments AISTATS 2020
Variational Optimization on Lie Groups, with Examples of Leading (Generalized) Eigenvalue Problems AISTATS 2020
Stochastic Recursive Variance-Reduced Cubic Regularization Methods AISTATS 2020