no code implementations • 2 May 2024 • Samyadeep Basu, Keivan Rezaei, Ryan Rossi, Cherry Zhao, Vlad Morariu, Varun Manjunatha, Soheil Feizi
To address this issue, we introduce the concept of Mechanistic Localization in text-to-image models, where knowledge about various visual attributes (e. g., ``style", ``objects", ``facts") can be mechanistically localized to a small fraction of layers in the UNet, thus facilitating efficient model editing.
1 code implementation • NeurIPS 2023 • Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin, Nesreen Ahmed, Christos Faloutsos
The choice of a graph learning (GL) model (i. e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks.
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.
1 code implementation • 16 Mar 2024 • Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
no code implementations • 31 Jan 2024 • Zhenghao Zeng, David Arbour, Avi Feller, Raghavendra Addanki, Ryan Rossi, Ritwik Sinha, Edward H. Kennedy
In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem.
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.
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.
1 code implementation • 18 Jun 2022 • Namyong Park, Ryan Rossi, Nesreen Ahmed, Christos Faloutsos
In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations.
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.
1 code implementation • 12 Dec 2021 • ZiHao Zhou, Xingyi Yang, Ryan Rossi, Handong Zhao, Rose Yu
The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.
no code implementations • NeurIPS 2021 • Yue Zhao, Ryan Rossi, Leman Akoglu
Given an unsupervised outlier detection task on a new dataset, how can we automatically select a good outlier detection algorithm and its hyperparameter(s) (collectively called a model)?
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.
no code implementations • 8 Mar 2021 • Mojtaba Sahraee-Ardakan, Tung Mai, Anup Rao, Ryan Rossi, Sundeep Rangan, Alyson K. Fletcher
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
no code implementations • 25 Feb 2021 • Enayat Ullah, Tung Mai, Anup Rao, Ryan Rossi, Raman Arora
Our key contribution is the design of corresponding efficient unlearning algorithms, which are based on constructing a (maximal) coupling of Markov chains for the noisy SGD procedure.
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.
no code implementations • 1 Jan 2021 • ZiHao Zhou, Xingyi Yang, Xinyi He, Ryan Rossi, Handong Zhao, Rose Yu
To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.
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 • 26 Sep 2020 • Chenhan Yuan, Ryan Rossi, Andrew Katz, Hoda Eldardiry
In this paper, we relax this strong assumption by a weaker distant supervision assumption to address the second issue and propose a novel sentence distribution estimator model to address the first problem.
no code implementations • 26 Sep 2020 • Chenhan Yuan, Ryan Rossi, Andrew Katz, Hoda Eldardiry
To address this issue, we propose a Clustering-based Unsupervised generative Relation Extraction (CURE) framework that leverages an "Encoder-Decoder" architecture to perform self-supervised learning so the encoder can extract relation information.
no code implementations • 21 Sep 2020 • Galen Weld, Peter West, Maria Glenski, David Arbour, Ryan Rossi, Tim Althoff
Across 648 experiments and two datasets, we evaluate every commonly used causal inference method and identify their strengths and weaknesses to inform social media researchers seeking to use such methods, and guide future improvements.
no code implementations • 16 Jan 2020 • Ryan Rossi, Somdeb Sarkhel, Nesreen Ahmed
We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
no code implementations • 25 Sep 2019 • Hongchang Gao, Gang Wu, Ryan Rossi, Viswanathan Swaminathan, Heng Huang
Factorization Machines (FMs) is an important supervised learning approach due to its unique ability to capture feature interactions when dealing with high-dimensional sparse data.
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.
1 code implementation • 18 Apr 2019 • Di Jin, Mark Heimann, Ryan Rossi, Danai Koutra
Identity stitching, the task of identifying and matching various online references (e. g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations.
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
1 code implementation • KDD 2018 • John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains.
Ranked #1 on Graph Classification on NCI33
2 code implementations • IJCAI 2018 • Nesreen K. Ahmed, Ryan Rossi, John Boaz Lee, Theodore L. Willke, Rong Zhou, Xiangnan Kong, Hoda Eldardiry
Random walks are at the heart of many existing network embedding methods.
no code implementations • 15 Sep 2017 • John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains.