4 citations · 8 across the 6 of their papers we have counts for
6 papers
Improved Stein Variational Gradient Descent with Importance Weights
Lukang Sun, Peter Richtárik
Stein Variational Gradient Descent (SVGD) is a popular sampling algorithm used in various machine learning tasks. It is well known that SVGD arises from a discretization of the ker…
A Note on the Convergence of Mirrored Stein Variational Gradient Descent under Smoothness Condition
Lukang Sun, Peter Richtárik
In this note, we establish a descent lemma for the population limit Mirrored Stein Variational Gradient Method~(MSVGD). This descent lemma does not rely on the path information of…
Federated Learning with a Sampling Algorithm under Isoperimetry
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. These techniqu…
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling
Alexander Tyurin, Lukang Sun, Konstantin Burlachenko +1
We revisit the classical problem of finding an approximately stationary point of the average of smooth and possibly nonconvex functions. The optimal complexity of stochastic fi…
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition
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 $π(x) \propto \exp(-V(…
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
Adil Salim, Lukang Sun, Peter Richtárik
Stein Variational Gradient Descent (SVGD) is an algorithm for sampling from a target density which is known up to a multiplicative constant. Although SVGD is a popular algorithm in…