52 citations · 104 across the 23 of their papers we have counts for
8 papers · 1 filter
On the Depth Transfer of Neural Optimizers via L-Smoothness
Maximilian Wong, Chenyu Xu, Jiajin Li +1
Scaling deep learning models efficiently requires predictable performance transfer across compute regimes. Establishing reliable hyperparameter transfer allows optimal training dyn…
Outlier-Robust Gromov-Wasserstein for Graph Data
Lemin Kong, Jiajin Li, Jianheng Tang +1
Gromov-Wasserstein (GW) distance is a powerful tool for comparing and aligning probability distributions supported on different metric spaces. Recently, GW has become the main mode…
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.…
Learning Proximal Operators to Discover Multiple Optima
Lingxiao Li, Noam Aigerman, Vladimir G. Kim +4
Finding multiple solutions of non-convex optimization problems is a ubiquitous yet challenging task. Most past algorithms either apply single-solution optimization methods from mul…
Deconvolutional Networks on Graph Data
Jia Li, Jiajin Li, Yang Liu +3
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct th…
Dirichlet Graph Variational Autoencoder
Jia Li, Tomasyu Yu, Jiajin Li +5
Graph Neural Networks (GNNs) and Variational Autoencoders (VAEs) have been widely used in modeling and generating graphs with latent factors. However, there is no clear explanation…
Policy Optimization with Second-Order Advantage Information
Jiajin Li, Baoxiang Wang
Policy optimization on high-dimensional continuous control tasks exhibits its difficulty caused by the large variance of the policy gradient estimators. We present the action subsp…