no code implementations • ACL 2022 • Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao
In this work, we study a more challenging but practical problem, i. e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.
no code implementations • 2 Apr 2024 • Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan A. Rossi, Anup Rao, Tung Mai, Shuai Li
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i. e., texts that are factually incorrect or unsupported.
no code implementations • 26 Mar 2024 • Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, C. Lee Giles, Ting-Hao K. Huang
Crafting effective captions for figures is important.
no code implementations • 11 Mar 2024 • Yu Xia, Fang Kong, Tong Yu, Liya Guo, Ryan A. Rossi, Sungchul Kim, Shuai Li
In this paper, we propose a time-increasing bandit algorithm TI-UCB, which effectively predicts the increase of model performances due to finetuning and efficiently balances exploration and exploitation in model selection.
no code implementations • 22 Feb 2024 • Younghun Lee, Sungchul Kim, Tong Yu, Ryan A. Rossi, Xiang Chen
The model learns to reduce the input context using On-Policy Reinforcement Learning and aims to improve the reasoning performance of a fixed LLM.
no code implementations • 3 Feb 2024 • Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Tong Yu, Hanieh Deilamsalehy, Ruiyi Zhang, Sungchul Kim, Franck Dernoncourt
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases.
1 code implementation • 28 Jan 2024 • Yujian Liu, Jiabao Ji, Tong Yu, Ryan Rossi, Sungchul Kim, Handong Zhao, Ritwik Sinha, Yang Zhang, Shiyu Chang
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
no code implementations • 29 Nov 2023 • Puja Trivedi, Ryan Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
no code implementations • 23 Oct 2023 • Ting-Yao Hsu, Chieh-Yang Huang, Ryan Rossi, Sungchul Kim, C. Lee Giles, Ting-Hao K. Huang
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
1 code implementation • 2 Sep 2023 • Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, Nesreen K. Ahmed
Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into systems that touch our social sphere.
1 code implementation • 20 Jul 2023 • Ashish Singh, Prateek Agarwal, Zixuan Huang, Arpita Singh, Tong Yu, Sungchul Kim, Victor Bursztyn, Nikos Vlassis, Ryan A. Rossi
Captions are crucial for understanding scientific visualizations and documents.
no code implementations • 8 Jul 2023 • April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, Nesreen K. Ahmed
In this article, we examine and categorize fairness techniques for improving the fairness of GNNs.
no code implementations • 28 Mar 2023 • Rashmi Ranjan Bhuyan, Adel Javanmard, Sungchul Kim, Gourab Mukherjee, Ryan A. Rossi, Tong Yu, Handong Zhao
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 segments.
no code implementations • 23 Feb 2023 • Chieh-Yang Huang, Ting-Yao Hsu, Ryan Rossi, Ani Nenkova, Sungchul Kim, Gromit Yeuk-Yin Chan, Eunyee Koh, Clyde Lee Giles, Ting-Hao 'Kenneth' Huang
Prior work often treated figure caption generation as a vision-to-language task.
no code implementations • 22 Dec 2022 • April Chen, Ryan Rossi, Nedim Lipka, Jane Hoffswell, Gromit Chan, Shunan Guo, Eunyee Koh, Sungchul Kim, Nesreen K. Ahmed
Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node.
no code implementations • 30 Sep 2022 • Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka, Shunan Guo, Cameron Musco
We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions.
1 code implementation • 18 Aug 2022 • Sejoon Oh, Ankur Bhardwaj, Jongseok Han, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
Session-based recommender systems capture the short-term interest of a user within a session.
1 code implementation • 26 Jul 2022 • Zhankui He, Handong Zhao, Tong Yu, Sungchul Kim, Fan Du, Julian McAuley
MCR, which uses a conversational paradigm to elicit user interests by asking user preferences on tags (e. g., categories or attributes) and handling user feedback across multiple rounds, is an emerging recommendation setting to acquire user feedback and narrow down the output space, but has not been explored in the context of bundle recommendation.
1 code implementation • 5 Apr 2022 • Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, Christos Faloutsos
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
no code implementations • 29 Sep 2021 • Mustafa Abdallah, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Handong Zhao, Haoliang Wang, Saurabh Bagchi
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
1 code implementation • 23 Aug 2021 • Sejoon Oh, Sungchul Kim, Ryan A. Rossi, Srijan Kumar
In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.
no code implementations • NAACL 2021 • Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan A. Rossi, Nedim Lipka, Sheng Li
Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods.
no code implementations • 12 Feb 2021 • Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Nesreen K. Ahmed
Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback.
no code implementations • 1 Jan 2021 • Jun Yan, Mrigank Raman, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
Recently, neural-symbolic architectures have achieved success on commonsense reasoning through effectively encoding relational structures retrieved from external knowledge graphs (KGs) and obtained state-of-the-art results in tasks such as (commonsense) question answering and natural language inference.
1 code implementation • Findings (ACL) 2021 • Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren
Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.
1 code implementation • ICLR 2021 • Mrigank Raman, Aaron Chan, Siddhant Agarwal, Peifeng Wang, Hansen Wang, Sungchul Kim, Ryan Rossi, Handong Zhao, Nedim Lipka, Xiang Ren
Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation.
no code implementations • 23 Oct 2020 • Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh
Notably, since typed graphlet is more general than colored graphlet (and untyped graphlets), the counts of various typed graphlets can be combined to obtain the counts of the much simpler notion of colored graphlets.
no code implementations • 14 Oct 2020 • Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Sungchul Kim, Hoda Eldardiry
GraphDF is a hybrid forecasting framework that consists of a relational global and relational local model.
no code implementations • 25 Sep 2020 • Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Joel Chan
Finally, we observed a strong preference by the human experts in our user study towards the visualizations recommended by our ML-based system as opposed to the rule-based system (5. 92 from a 7-point Likert scale compared to only 3. 45).
no code implementations • 21 Sep 2020 • Di Jin, Sungchul Kim, Ryan A. Rossi, Danai Koutra
While previous work on dynamic modeling and embedding has focused on representing a stream of timestamped edges using a time-series of graphs based on a specific time-scale (e. g., 1 month), we propose the notion of an $\epsilon$-graph time-series that uses a fixed number of edges for each graph, and show its superiority over the time-scale representation used in previous work.
no code implementations • 22 Aug 2019 • Ryan A. Rossi, Di Jin, Sungchul Kim, Nesreen K. Ahmed, Danai Koutra, John Boaz Lee
Unfortunately, recent work has sometimes confused the notion of structural roles and communities (based on proximity) leading to misleading or incorrect claims about the capabilities of network embedding methods.
no code implementations • 12 Jun 2019 • Ryan A. Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu
In this work, we investigate higher-order network motifs and develop techniques based on the notion of closing higher-order motifs that move beyond closing simple triangles.
no code implementations • 7 Jun 2019 • Charles Chen, Ruiyi Zhang, Eunyee Koh, Sungchul Kim, Scott Cohen, Tong Yu, Ryan Rossi, Razvan Bunescu
In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure.
no code implementations • 12 Apr 2019 • John Boaz Lee, Giang Nguyen, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim
In this work, we propose using the notion of temporal walks for learning dynamic embeddings from temporal networks.
no code implementations • 28 Jan 2019 • Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh
To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks.
1 code implementation • 11 Nov 2018 • Di Jin, Ryan Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim, Anup Rao
Motivated by the computational and storage challenges that dense embeddings pose, we introduce the problem of latent network summarization that aims to learn a compact, latent representation of the graph structure with dimensionality that is independent of the input graph size (i. e., #nodes and #edges), while retaining the ability to derive node representations on the fly.
Social and Information Networks
no code implementations • 12 Sep 2018 • John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao
Experiments show that our proposed method is able to achieve state-of-the-art results on the semi-supervised node classification task.
1 code implementation • 20 Jul 2018 • John Boaz Lee, Ryan A. Rossi, Sungchul Kim, Nesreen K. Ahmed, Eunyee Koh
However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.
no code implementations • 28 Jan 2018 • Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, Yasin Abbasi Yadkori
This paper describes a general framework for learning Higher-Order Network Embeddings (HONE) from graph data based on network motifs.