Search Results for author: Ryan A. Rossi

Found 59 papers, 11 papers with code

Hallucination Diversity-Aware Active Learning for Text Summarization

no code implementations2 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.

Active Learning Hallucination +1

Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits

no code implementations11 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.

Change Detection Model Selection

Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models

no code implementations22 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.

Language Modelling

Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constraint

no code implementations4 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).

Stochastic Optimization

Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes

no code implementations3 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.

Text Generation Zero-Shot Learning

ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search

no code implementations20 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.

Decision Making valid

PDFTriage: Question Answering over Long, Structured Documents

no code implementations16 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.

Question Answering Retrieval

Bias and Fairness in Large Language Models: A Survey

1 code implementation2 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.

counterfactual Fairness

Knowledge Graph Prompting for Multi-Document Question Answering

1 code implementation22 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.

graph construction Open-Domain Question Answering +1

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

2 code implementations29 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.

A Model-free Closeness-of-influence Test for Features in Supervised Learning

no code implementations20 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.

Binary Classification

Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model

no code implementations28 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.

A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings

no code implementations28 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.

Hyperedge Prediction Node Classification +1

PersonaSAGE: A Multi-Persona Graph Neural Network

no code implementations28 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.

Link Prediction Management +1

Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions

no code implementations4 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.

Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs

no code implementations30 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.

Edge Classification Link Prediction

Network Report: A Structured Description for Network Datasets

no code implementations8 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.

CyCLIP: Cyclic Contrastive Language-Image Pretraining

1 code implementation28 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.

Representation Learning Visual Reasoning +1

Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes

no code implementations29 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.

Point Processes valid

Influence-guided Data Augmentation for Neural Tensor Completion

1 code implementation23 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.

Data Augmentation Imputation +2

Insight-centric Visualization Recommendation

no code implementations21 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.

Attribute Recommendation Systems

Personalized Visualization Recommendation

no code implementations12 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.

Fundamental Tradeoffs in Distributionally Adversarial Training

no code implementations15 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.

Binary Classification regression

Heterogeneous Graphlets

no code implementations23 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.

Graph Deep Factors for Forecasting

no code implementations14 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.

Computational Efficiency Time Series +1

Graph Neural Networks with Heterophily

1 code implementation28 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.

A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting

no code implementations25 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.

Irregular Time Series Time Series +1

ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data

no code implementations25 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).

Automating Outlier Detection via Meta-Learning

1 code implementation22 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)?

Anomaly Detection AutoML +3

From Static to Dynamic Node Embeddings

no code implementations21 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.

Time Series Time Series Analysis

Structured Policy Iteration for Linear Quadratic Regulator

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.

Temporal Network Sampling

no code implementations18 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.

Descriptive Time Series +1

On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications

no code implementations22 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.

Misconceptions Network Embedding

Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs

no code implementations12 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.

Link Prediction

Dynamic Node Embeddings from Edge Streams

no code implementations12 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.

Representation Learning valid

Heterogeneous Network Motifs

no code implementations28 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.

Higher-order Spectral Clustering for Heterogeneous Graphs

no code implementations6 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.

Clustering Link Prediction

Higher-order Graph Convolutional Networks

no code implementations12 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.

General Classification Graph Attention +1

Attention Models in Graphs: A Survey

1 code implementation20 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.

Graph Attention Graph Classification +2

Predicting Graph Categories from Structural Properties

no code implementations7 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.

General Classification

HONE: Higher-Order Network Embeddings

no code implementations28 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.

Inductive Representation Learning in Large Attributed Graphs

no code implementations25 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 $w$.

Anomaly Detection Attribute +2

Network Classification and Categorization

no code implementations13 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).

Classification General Classification

Deep Feature Learning for Graphs

no code implementations28 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.

Graph Representation Learning Transfer Learning

Estimation of Graphlet Statistics

no code implementations6 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.

Revisiting Role Discovery in Networks: From Node to Edge Roles

no code implementations4 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.

Hybrid CPU-GPU Framework for Network Motifs

no code implementations18 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).

Relational Similarity Machines

no code implementations2 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.

General Classification Multi-class Classification +1

Graphlet Decomposition: Framework, Algorithms, and Applications

no code implementations13 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.

A Web-based Interactive Visual Graph Analytics Platform

no code implementations2 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.

Community Detection Decision Making +1

Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage

1 code implementation25 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

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