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Found 15 papers, 3 papers with code
Aligned Diffusion Schrödinger Bridges
Diffusion Schr\"odinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents.
Unbalanced Diffusion Schrödinger Bridge
Schr\"odinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems.
Mirrored Langevin Dynamics
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design.
Dimension-free Information Concentration via Exp-Concavity
Information concentration of probability measures have important implications in learning theory.
Preconditioned Spectral Descent for Deep Learning
These challenges include, but are not limited to, the non-convexity of learning objectives and estimating the quantities needed for optimization algorithms, such as gradients.
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
We propose a simple algorithmic framework that simultaneously achieves the best rates for honest regret as well as adversarial regret, and in addition resolves the open problem of removing the logarithmic terms in convergence to the value of the game.
Finding Mixed Nash Equilibria of Generative Adversarial Networks
We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective.
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenomena.
Conditional gradient methods for stochastically constrained convex minimization
We propose two novel conditional gradient-based methods for solving structured stochastic convex optimization problems with a large number of linear constraints.
The Schrödinger Bridge between Gaussian Measures has a Closed Form
The static optimal transport $(\mathrm{OT})$ problem between Gaussians seeks to recover an optimal map, or more generally a coupling, to morph a Gaussian into another.
A unified stochastic approximation framework for learning in games
We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite).
Riemannian stochastic approximation algorithms
We examine a wide class of stochastic approximation algorithms for solving (stochastic) nonlinear problems on Riemannian manifolds.
Continuous-time Analysis for Variational Inequalities: An Overview and Desiderata
Algorithms that solve zero-sum games, multi-objective agent objectives, or, more generally, variational inequality (VI) problems are notoriously unstable on general problems.
Sinkhorn Flow: A Continuous-Time Framework for Understanding and Generalizing the Sinkhorn Algorithm
Many problems in machine learning can be formulated as solving entropy-regularized optimal transport on the space of probability measures.
