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Found 7 papers, 2 papers with code
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation
Exact Bayesian inference on state-space models~(SSM) is in general untractable, and unfortunately, basic Sequential Monte Carlo~(SMC) methods do not yield correct approximations for complex models.
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
For generality, we focus on local solutions of constrained, non-monotone variational inequalities, and we show that the convergence rate of a given method depends sharply on its associated Legendre exponent, a notion that measures the growth rate of the underlying Bregman function (Euclidean, entropic, or other) near a solution.
The Last-Iterate Convergence Rate of Optimistic Mirror Descent in Stochastic Variational Inequalities
In this paper, we analyze the local convergence rate of optimistic mirror descent methods in stochastic variational inequalities, a class of optimization problems with important applications to learning theory and machine learning.
Expressive Power of Invariant and Equivariant Graph Neural Networks
Various classes of Graph Neural Networks (GNN) have been proposed and shown to be successful in a wide range of applications with graph structured data.
Accelerating Smooth Games by Manipulating Spectral Shapes
Using this perspective, we propose an optimal algorithm for bilinear games.
Linear Lower Bounds and Conditioning of Differentiable Games
In this work, we approach the question of fundamental iteration complexity by providing lower bounds to complement the linear (i. e. geometric) upper bounds observed in the literature on a wide class of problems.
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Games
We provide new analyses of the EG's local and global convergence properties and use is to get a tighter global convergence rate for OG and CO. Our analysis covers the whole range of settings between bilinear and strongly monotone games.
