Search Results for author:
Found 6 papers, 2 papers with code
Improved Stein Variational Gradient Descent with Importance Weights
In the continuous time and infinite particles regime, the time for this flow to converge to the equilibrium distribution $\pi$, quantified by the Stein Fisher information, depends on $\rho_0$ and $\pi$ very weakly.
A Note on the Convergence of Mirrored Stein Variational Gradient Descent under $(L_0,L_1)-$Smoothness Condition
In this note, we establish a descent lemma for the population limit Mirrored Stein Variational Gradient Method~(MSVGD).
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling
The optimal complexity of stochastic first-order methods in terms of the number of gradient evaluations of individual functions is $\mathcal{O}\left(n + n^{1/2}\varepsilon^{-1}\right)$, attained by the optimal SGD methods $\small\sf\color{green}{SPIDER}$(arXiv:1807. 01695) and $\small\sf\color{green}{PAGE}$(arXiv:2008. 10898), for example, where $\varepsilon$ is the error tolerance.
Federated Learning with a Sampling Algorithm under Isoperimetry
Federated learning uses a set of techniques to efficiently distribute the training of a machine learning algorithm across several devices, who own the training data.
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition
Stein Variational Gradient Descent (SVGD) is an important alternative to the Langevin-type algorithms for sampling from probability distributions of the form $\pi(x) \propto \exp(-V(x))$.
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
We first establish the convergence of the algorithm.
