79 citations · 364 across the 27 of their papers we have counts for
28 papers
Bias and Fairness in Large Language Models: A Survey
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touc…
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…
FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback
Ashish Singh, Ashutosh Singh, Prateek Agarwal +10
Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from docum…
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model
Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim +4
We consider dynamic pricing strategies in a streamed longitudinal data set-up where the objective is to maximize, over time, the cumulative profit across a large number of customer…
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi +6
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation…
Graph Learning with Localized Neighborhood Fairness
April Chen, Ryan Rossi, Nedim Lipka +6
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level b…
Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs
Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim +5
Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit…
Implicit Session Contexts for Next-Item Recommendations
Sejoon Oh, Ankur Bhardwaj, Jongseok Han +3
Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are…
Bundle MCR: Towards Conversational Bundle Recommendation
Zhankui He, Handong Zhao, Tong Yu +3
Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large out…
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Namyong Park, Ryan Rossi, Eunyee Koh +5
Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper,…
Influence-guided Data Augmentation for Neural Tensor Completion
Sejoon Oh, Sungchul Kim, Ryan A. Rossi +1
How can we predict missing values in multi-dimensional data (or tensors) more accurately? The task of tensor completion is crucial in many applications such as personalized recomme…
Edge: Enriching Knowledge Graph Embeddings with External Text
Saed Rezayi, Handong Zhao, Sungchul Kim +3
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the…
Personalized Visualization Recommendation
Xin Qian, Ryan A. Rossi, Fan Du +5
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These…
Heterogeneous Graphlets
Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza +4
In this paper, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed gra…
Graph Deep Factors for Forecasting
Hongjie Chen, Ryan A. Rossi, Kanak Mahadik +2
Deep probabilistic forecasting techniques have recently been proposed for modeling large collections of time-series. However, these techniques explicitly assume either complete ind…
Learning Contextualized Knowledge Structures for Commonsense Reasoning
Jun Yan, Mrigank Raman, Aaron Chan +6
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. However, KG edge (fact) sparsity and noisy edge extraction/…
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
Mrigank Raman, Aaron Chan, Siddhant Agarwal +7
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over…
ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data
Xin Qian, Ryan A. Rossi, Fan Du +5
Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insight…
From Static to Dynamic Node Embeddings
Di Jin, Sungchul Kim, Ryan A. Rossi +1
We introduce a general framework for leveraging graph stream data for temporal prediction-based applications. Our proposed framework includes novel methods for learning an appropri…
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications
Ryan A. Rossi, Di Jin, Sungchul Kim +3
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connection…
Linear-time Hierarchical Community Detection
Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh +1
Community detection in graphs has many important and fundamental applications including in distributed systems, compression, image segmentation, divide-and-conquer graph algorithms…
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs
Ryan A. Rossi, Anup Rao, Sungchul Kim +3
In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. Th…
Figure Captioning with Reasoning and Sequence-Level Training
Charles Chen, Ruiyi Zhang, Eunyee Koh +5
Figures, such as bar charts, pie charts, and line plots, are widely used to convey important information in a concise format. They are usually human-friendly but difficult for comp…
Dynamic Node Embeddings from Edge Streams
John Boaz Lee, Giang Nguyen, Ryan A. Rossi +3
Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Such temporal networks (or edge streams) consist of a sequence of timestamped e…