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Stochastic Methods

1,118 papers

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Papers

Regret Minimization in Stochastic Non-Convex Learning via a Proximal-Gradient Approach ICML 2021
Multiplicative Noise and Heavy Tails in Stochastic Optimization ICML 2021
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference ICML 2021
Bias-Robust Bayesian Optimization via Dueling Bandits ICML 2021
Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions ICML 2021
Stability and Generalization of Stochastic Gradient Methods for Minimax Problems ICML 2021
Quantization Algorithms for Random Fourier Features ICML 2021
Stochastic Iterative Graph Matching ICML 2021
Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness ICML 2021
Best Arm Identification in Graphical Bilinear Bandits ICML 2021
Parallel tempering on optimized paths ICML 2021
SGLB: Stochastic Gradient Langevin Boosting ICML 2021
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies ICML 2021
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization ICML 2021
A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization ICML 2021
BASGD: Buffered Asynchronous SGD for Byzantine Learning ICML 2021
Quantile Bandits for Best Arms Identification ICML 2021
Fast Multi-label Learning IJCAI 2021
Simple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent IJCNLP 2021
Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions JMLR 2021
From Low Probability to High Confidence in Stochastic Convex Optimization JMLR 2021
Stochastic Proximal AUC Maximization JMLR 2021
Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021
L-SVRG and L-Katyusha with Arbitrary Sampling JMLR 2021
Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization JMLR 2021