1 code implementation • 2 Oct 2022 • Lukang Sun, Peter Richtárik
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.
no code implementations • 20 Jun 2022 • Lukang Sun, Peter Richtárik
In this note, we establish a descent lemma for the population limit Mirrored Stein Variational Gradient Method~(MSVGD).
1 code implementation • 5 Jun 2022 • Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik
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.
no code implementations • 2 Jun 2022 • Lukang Sun, Adil Salim, Peter Richtárik
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.
no code implementations • 1 Jun 2022 • Lukang Sun, Avetik Karagulyan, Peter Richtarik
Stein Variational Gradient Descent (SVGD) is an important alternative to the Langevin-type algorithms for sampling from probability distributions of the form $\pi(x) \propto \exp(-V(x))$.
no code implementations • 6 Jun 2021 • Adil Salim, Lukang Sun, Peter Richtárik
We first establish the convergence of the algorithm.