463 citations · 5.1k across the 190 of their papers we have counts for
14 papers · 2 filters
Dynamic Embedding on Textual Networks via a Gaussian Process
Pengyu Cheng, Yitong Li, Xinyuan Zhang +3
Textual network embedding aims to learn low-dimensional representations of text-annotated nodes in a graph. Prior work in this area has typically focused on fixed graph structures;…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
Straight-Through Estimator as Projected Wasserstein Gradient Flow
Pengyu Cheng, Chang Liu, Chunyuan Li +3
The Straight-Through (ST) estimator is a widely used technique for back-propagating gradients through discrete random variables. However, this effective method lacks theoretical ju…
Fused Gromov-Wasserstein Alignment for Hawkes Processes
Dixin Luo, Hongteng Xu, Lawrence Carin
We propose a novel fused Gromov-Wasserstein alignment method to jointly learn the Hawkes processes in different event spaces, and align their event types. Given two Hawkes processe…
Zero-Shot Recognition via Optimal Transport
Wenlin Wang, Hongteng Xu, Guoyin Wang +2
We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxili…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation
Shuyang Dai, Yu Cheng, Yizhe Zhang +3
Recent unsupervised approaches to domain adaptation primarily focus on minimizing the gap between the source and the target domains through refining the feature generator, in order…
Towards Amortized Ranking-Critical Training for Collaborative Filtering
Sam Lobel, Chunyuan Li, Jianfeng Gao +1
Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating f…
On Norm-Agnostic Robustness of Adversarial Training
Bai Li, Changyou Chen, Wenlin Wang +1
Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where ad…
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin, Piyush Rai
Stochastic blockmodels (SBM) and their variants, , mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have…
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Hongteng Xu, Dixin Luo, Lawrence Carin
We propose a scalable Gromov-Wasserstein learning (S-GWL) method and establish a novel and theoretically-supported paradigm for large-scale graph analysis. The proposed method is b…
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Hao Fu, Chunyuan Li, Xiaodong Liu +3
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) rec…
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li +3
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples. With insights so garnered, adversarial learning…
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Hongteng Xu, Dixin Luo, Hongyuan Zha +1
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discr…