18 citations · 39 across the 11 of their papers we have counts for
11 papers
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…
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
Jiaojiao Fan, Haotian Xue, Qinsheng Zhang +1
There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models. Among various methods, recent works have found that simply m…
On a Class of Gibbs Sampling over Networks
Bo Yuan, Jiaojiao Fan, Jiaming Liang +2
We consider the sampling problem from a composite distribution whose potential (negative log density) is $\sum_{i=1}^n f_i(x_i)+\sum_{j=1}^m g_j(y_j)+\sum_{i=1}^n\sum_{j=1}^m\frac{…
Generating Synthetic Datasets by Interpolating along Generalized Geodesics
Jiaojiao Fan, David Alvarez-Melis
Data for pretraining machine learning models often consists of collections of heterogeneous datasets. Although training on their union is reasonable in agnostic settings, it might…
Improved dimension dependence of a proximal algorithm for sampling
Jiaojiao Fan, Bo Yuan, Yongxin Chen
We propose a sampling algorithm that achieves superior complexity bounds in all the classical settings (strongly log-concave, log-concave, Logarithmic-Sobolev inequality (LSI), Poi…
Nesterov smoothing for sampling without smoothness
Jiaojiao Fan, Bo Yuan, Jiaming Liang +1
We study the problem of sampling from a target distribution in whose potential is not smooth. Compared with the sampling problem with smooth potentials, this problem…
Variational Wasserstein gradient flow
Jiaojiao Fan, Qinsheng Zhang, Amirhossein Taghvaei +1
Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions. A recent trend is to use the well-known JK…
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…
Stein particle filtering
Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen
We present a new particle filtering algorithm for nonlinear systems in the discrete-time setting. Our algorithm is based on the Stein variational gradient descent (SVGD) framework,…
Neural Monge Map estimation and its applications
Jiaojiao Fan, Shu Liu, Shaojun Ma +2
Monge map refers to the optimal transport map between two probability distributions and provides a principled approach to transform one distribution to another. Neural network base…
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen
Wasserstein Barycenter is a principled approach to represent the weighted mean of a given set of probability distributions, utilizing the geometry induced by optimal transport. In…