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 • 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 • 4 Feb 2024 • Harshita Chopra, Atanu R. Sinha, Sunav Choudhary, Ryan A. Rossi, Paavan Kumar Indela, Veda Pranav Parwatala, Srinjayee Paul, Aurghya Maiti
Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure).
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.
no code implementations • 20 Oct 2023 • Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, Chao Zhang
It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan.
no code implementations • 17 Sep 2023 • Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, Hieu Man, Nghia Trung Ngo, Franck Dernoncourt, Ryan A. Rossi, Thien Huu Nguyen
However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed.
no code implementations • 16 Sep 2023 • Jon Saad-Falcon, Joe Barrow, Alexa Siu, Ani Nenkova, David Seunghyun Yoon, Ryan A. Rossi, Franck Dernoncourt
Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure.
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 • 22 Aug 2023 • Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr
Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.
2 code implementations • 29 Jul 2023 • Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo, Thuat Nguyen, Franck Dernoncourt, Ryan A. Rossi, Thien Huu Nguyen
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
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 • 20 Jun 2023 • Mohammad Mehrabi, Ryan A. Rossi
Ideally, it is desired to understand how a set of collected features combine together and influence the response value, but this problem is notoriously difficult, due to the high-dimensionality of data and limited number of labeled data points, among many others.
no code implementations • 11 May 2023 • Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer, Jiuxiang Gu, Ani Nenkova
Visual text evokes an image in a person's mind, while non-visual text fails to do so.
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 • 28 Dec 2022 • Gautam Choudhary, Iftikhar Ahamath Burhanuddin, Eunyee Koh, Fan Du, Ryan A. Rossi
Furthermore, PersonaSAGE learns the appropriate set of persona embeddings for each node in the graph, and every node can have a different number of assigned persona embeddings.
no code implementations • 28 Dec 2022 • Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit Chan, Eunyee Koh, Nesreen Ahmed
In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph.
no code implementations • 4 Nov 2022 • Gaurav Verma, Vishwa Vinay, Ryan A. Rossi, Srijan Kumar
Our work aims to highlight and encourage further research on the robustness of deep multimodal models to realistic variations, especially in human-facing societal applications.
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.
no code implementations • 8 Jun 2022 • Xinyi Zheng, Ryan A. Rossi, Nesreen Ahmed, Dominik Moritz
Challenges arise as networks are often used across different domains (e. g., network science, physics, etc) and have complex structures.
1 code implementation • 28 May 2022 • Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi, Vishwa Vinay, Aditya Grover
Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness.
no code implementations • 29 Nov 2021 • Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen Ahmed
In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory.
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 • 21 Mar 2021 • Camille Harris, Ryan A. Rossi, Sana Malik, Jane Hoffswell, Fan Du, Tak Yeon Lee, Eunyee Koh, Handong Zhao
This global ranking makes it difficult and time-consuming for users to find the most interesting or relevant insights.
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 • 15 Jan 2021 • Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai
We study the tradeoff between standard risk and adversarial risk and derive the Pareto-optimal tradeoff, achievable over specific classes of models, in the infinite data limit with features dimension kept fixed.
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.
1 code implementation • 28 Sep 2020 • Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra
Graph Neural Networks (GNNs) have proven to be useful for many different practical applications.
no code implementations • 25 Sep 2020 • Hongjie Chen, Ryan A. Rossi, Kanak Mahadik, Hoda Eldardiry
We propose a novel context integrated relational model, Context Integrated Graph Neural Network (CIGNN), which leverages the temporal, relational, spatial, and dynamic contextual dependencies for multi-step ahead demand forecasting.
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).
1 code implementation • 22 Sep 2020 • Yue Zhao, Ryan A. Rossi, Leman Akoglu
Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)?
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 • ICML 2020 • Youngsuk Park, Ryan A. Rossi, Zheng Wen, Gang Wu, Handong Zhao
In this paper, we introduce the \textit{Structured Policy Iteration} (S-PI) for LQR, a method capable of deriving a structured linear policy.
no code implementations • 18 Oct 2019 • Nesreen K. Ahmed, Nick Duffield, Ryan A. Rossi
In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally-weighted.
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 • 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.
no code implementations • 6 Oct 2018 • Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh
Using typed-graphlets as a basis, we develop a general principled framework for higher-order clustering in heterogeneous 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 • 7 May 2018 • James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan
Even though the current version of this paper is withdrawn, there was no disagreement between authors on the novel work in this paper.
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.
no code implementations • 27 Oct 2017 • Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry, Rong Zhou
Multi-label classification is an important learning problem with many applications.
no code implementations • 25 Oct 2017 • Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
To make these methods more generally applicable, we propose a framework for inductive network representation learning based on the notion of attributed random walk that is not tied to node identity and is instead based on learning a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute vector $\mathrm{\rm \bf x}$ to a type $w$.
no code implementations • 14 Sep 2017 • Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
Random walks are at the heart of many existing deep learning algorithms for graph data.
no code implementations • 13 Sep 2017 • James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e. g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs).
no code implementations • 28 Apr 2017 • Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
This paper presents a general graph representation learning framework called DeepGL for learning deep node and edge representations from large (attributed) graphs.
no code implementations • 6 Jan 2017 • Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
In this work, we propose an unbiased graphlet estimation framework that is (a) fast with significant speedups compared to the state-of-the-art, (b) parallel with nearly linear-speedups, (c) accurate with <1% relative error, (d) scalable and space-efficient for massive networks with billions of edges, and (e) flexible for a variety of real-world settings, as well as estimating macro and micro-level graphlet statistics (e. g., counts) of both connected and disconnected graphlets.
no code implementations • 4 Oct 2016 • Nesreen K. Ahmed, Ryan A. Rossi, Theodore L. Willke, Rong Zhou
The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.
no code implementations • 18 Aug 2016 • Ryan A. Rossi, Rong Zhou
In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graphlets).
no code implementations • 2 Aug 2016 • Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks.
no code implementations • 13 Jun 2015 • Nesreen K. Ahmed, Jennifer Neville, Ryan A. Rossi, Nick Duffield, Theodore L. Willke
From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level.
no code implementations • 2 Feb 2015 • Nesreen K. Ahmed, Ryan A. Rossi
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks.
1 code implementation • 25 Feb 2013 • Ryan A. Rossi, David F. Gleich, Assefaw H. Gebremedhin, Md. Mostofa Ali Patwary
We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks.
Social and Information Networks Distributed, Parallel, and Cluster Computing Discrete Mathematics Data Structures and Algorithms Physics and Society 05C69 G.2.2