22 citations · 59 across the 17 of their papers we have counts for
19 papers
Optimal transport of image sources for interpolation of room impulse responses with moving sources
Jesper Brunnström, Filip Elvander, Isabel Haasler
In geometrical acoustics, room impulse responses (RIRs) can be represented by a set of image sources in free space. For a fixed source position, the image sources allow for computi…
Acoustic Image Source Interpolation with Optimal Transport Barycenter
Yuyang Liu, Rumeshika Pallewela, Jesper Brunnström +2
Room impulse responses can be estimated via the image source model (ISM) using the image source point cloud (ISPC) of a physical source. However, because the source movement change…
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport
Linda Fabiani, Sebastian J. Schlecht, Isabel Haasler +1
Separating sources is a common challenge in applications such as speech enhancement and telecommunications, where distinguishing between overlapping sounds helps reduce interferenc…
Mixtures of ensembles: System separation and identification via optimal transport
Filip Elvander, Isabel Haasler
Crowd dynamics and many large biological systems can be described as populations of agents or particles, which can only be observed on aggregate population level. Identifying the d…
Subgraph Matching via Partial Optimal Transport
Wen-Xin Pan, Isabel Haasler, Pascal Frossard
In this work, we propose a novel approach for subgraph matching, the problem of finding a given query graph in a large source graph, based on the fused Gromov-Wasserstein distance.…
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…
Multi-frequency tracking via group-sparse optimal transport
Isabel Haasler, Filip Elvander
In this work, we introduce an optimal transport framework for inferring power distributions over both spatial location and temporal frequency. Recently, it has been shown that opti…
Mean field type control with species dependent dynamics via structured tensor optimization
Axel Ringh, Isabel Haasler, Yongxin Chen +1
In this work we consider mean field type control problems with multiple species that have different dynamics. We formulate the discretized problem using a new type of entropy-regul…
Bures-Wasserstein Means of Graphs
Isabel Haasler, Pascal Frossard
Finding the mean of sampled data is a fundamental task in machine learning and statistics. However, in cases where the data samples are graph objects, defining a mean is an inheren…
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…
Incremental inference of collective graphical models
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chai…
Multi-marginal optimal transport and probabilistic graphical models
Isabel Haasler, Rahul Singh, Qinsheng Zhang +2
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for…
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…
Multi-Marginal Optimal Mass Transport with Partial Information
Filip Elvander, Isabel Haasler, Andreas Jakobsson +1
During recent decades, there has been a substantial development in optimal mass transport theory and methods. In this work, we consider multi-marginal problems wherein only partial…
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…
Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport
Filip Elvander, Isabel Haasler, Andreas Jakobsson +1
This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of…