28 citations · 81 across the 35 of their papers we have counts for
25 papers · 1 filter
Swarm Telepathy: Prefill-Free KV-Cache Transfer Across Families
Abdulrahman Alabdulkareem, Irene Tenison, Yang Zhang +3
LLM multi-agent systems split work across agents that communicate through text. An agent summarizes its findings, another agent receives the text. This constrains communication to…
VisAudit: Evaluating Multimodal Agents for Visual Diagnosis and Repair
Shicheng Liu, Adam Kahirov, Qi Zhang +5
Multimodal agents are increasingly used for data visualization tasks but remain limited in autonomous review. Unlike humans, they may fail to recognize when a visualization is inco…
How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge
Junhong Lin, Bing Zhang, Song Wang +4
Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid extern…
HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
Shuaicheng Zhang, Haohui Wang, Junhong Lin +5
Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filter…
Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs
Junhong Lin, Song Wang, Xiaojie Guo +2
Large language models (LLMs) excel at many language understanding tasks but struggle to reason over knowledge that evolves. To address this, recent work has explored augmenting LLM…
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
Zhining Liu, Ze Yang, Xiao Lin +6
Time-series forecasting plays a critical role in many real-world applications. Although increasingly powerful models have been developed and achieved superior results on benchmark…
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
Zhining Liu, Zihao Li, Ze Yang +6
Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we presen…
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Dongqi Fu, Yada Zhu, Zhining Liu +10
Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format o…
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
Song Wang, Junhong Lin, Xiaojie Guo +3
While large language models (LLMs) have made significant progress in processing and reasoning over knowledge graphs, current methods suffer from a high non-retrieval rate. This lim…
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
Zihao Li, Xiao Lin, Zhining Liu +8
While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, rema…
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Junhong Lin, Xiaojie Guo, Shuaicheng Zhang +2
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node a…
AIM: Attributing, Interpreting, Mitigating Data Unfairness
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +3
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals. Existing fair machine learning (FairML) research has pre…
Neural Active Learning Beyond Bandits
Yikun Ban, Ishika Agarwal, Ziwei Wu +4
We study both stream-based and pool-based active learning with neural network approximations. A recent line of works proposed bandit-based approaches that transformed active learni…
Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang, Yushun Dong, Chen Chen +3
Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based appli…
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…
FairGen: Towards Fair Graph Generation
Lecheng Zheng, Dawei Zhou, Hanghang Tong +3
There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks,…
Fairness-aware Multi-view Clustering
Lecheng Zheng, Yada Zhu, Jingrui He
In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), t…
STERLING: Synergistic Representation Learning on Bipartite Graphs
Baoyu Jing, Yuchen Yan, Kaize Ding +4
A fundamental challenge of bipartite graph representation learning is how to extract informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to address…
ARIEL: Adversarial Graph Contrastive Learning
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and ne…
COIN: Co-Cluster Infomax for Bipartite Graphs
Baoyu Jing, Yuchen Yan, Yada Zhu +1
Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, info…
Adversarial Graph Contrastive Learning with Information Regularization
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from…
Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis
Zixuan Yuan, Yada Zhu, Wei Zhang +3
Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in…
Heterogeneous Contrastive Learning
Lecheng Zheng, Jinjun Xiong, Yada Zhu +1
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multip…