22 citations · 44 across the 8 of their papers we have counts for
8 papers
Learning Sampled-data Control for Swarms via MeanFlow
Anqi Dong, Yongxin Chen, Karl H. Johansson +1
Steering large-scale swarms with only limited control updates is often needed due to communication or computational constraints, yet most learning-based approaches do not account f…
A parallel framework for graphical optimal transport
Jiaojiao Fan, Isabel Haasler, Qinsheng Zhang +2
We study multi-marginal optimal transport (MOT) problems where the underlying cost has a graphical structure. These graphical multi-marginal optimal transport problems have found a…
Graph-structured tensor optimization for nonlinear density control and mean field games
Axel Ringh, Isabel Haasler, Yongxin Chen +1
In this work we develop a numerical method for solving a type of convex graph-structured tensor optimization problems. This type of problems, which can be seen as a generalization…
On the complexity of the optimal transport problem with graph-structured cost
Jiaojiao Fan, Isabel Haasler, Johan Karlsson +1
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning…
Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport
Isabel Haasler, Axel Ringh, Yongxin Chen +1
In this work, we develop a new framework for dynamic network flow problems based on optimal transport theory. We show that the dynamic multi-commodity minimum-cost network flow pro…
Multi-marginal Optimal Transport with a Tree-structured cost and the Schrödinger Bridge Problem
Isabel Haasler, Axel Ringh, Yongxin Chen +1
The optimal transport problem has recently developed into a powerful framework for various applications in estimation and control. Many of the recent advances in the theory and app…
Inference with Aggregate Data: An Optimal Transport Approach
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief…
Estimating ensemble flows on a hidden Markov chain
Isabel Haasler, Axel Ringh, Yongxin Chen +1
We propose a new framework to estimate the evolution of an ensemble of indistinguishable agents on a hidden Markov chain using only aggregate output data. This work can be viewed a…