4.8k citations · 7k across the 47 of their papers we have counts for
47 papers
Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets
Yawen Guo, Rachael Zehrung, Katie Genuario +4
Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care i…
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Cheng Li, Mingyang Zhang, Qiaozhu Mei +4
Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designin…
Emoji Promotes Developer Participation and Issue Resolution on GitHub
Yuhang Zhou, Xuan Lu, Ge Gao +2
Although remote working is increasingly adopted during the pandemic, many are concerned by the low-efficiency in the remote working. Missing in text-based communication are non-ver…
Ranking & Reweighting Improves Group Distributional Robustness
Yachuan Liu, Bohan Zhang, Qiaozhu Mei +1
Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented gro…
A Prompt Log Analysis of Text-to-Image Generation Systems
Yutong Xie, Zhaoying Pan, Jinge Ma +2
Recent developments in large language models (LLM) and generative AI have unleashed the astonishing capabilities of text-to-image generation systems to synthesize high-quality imag…
Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
Xuan Lu, Wei Ai, Yixin Wang +1
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this an…
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Jiaqi Ma, Xingjian Zhang, Hezheng Fan +6
Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader…
Why is constrained neural language generation particularly challenging?
Cristina Garbacea, Qiaozhu Mei
Recent advances in deep neural language models combined with the capacity of large scale datasets have accelerated the development of natural language generation systems that produ…
Partition-Based Active Learning for Graph Neural Networks
Jiaqi Ma, Ziqiao Ma, Joyce Chai +1
We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup. We propose GraphPart, a novel partition-based active learning approa…
Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling
Jiaqi Ma, Xingjian Zhang, Qiaozhu Mei
Multinomial Logit (MNL) is one of the most popular discrete choice models and has been widely used to model ranking data. However, there is a long-standing technical challenge of l…
How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules
Yutong Xie, Ziqiao Xu, Jiaqi Ma +1
Forming a molecular candidate set that contains a wide range of potentially effective compounds is crucial to the success of drug discovery. While most databases and machine-learni…
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem
Jiaqi Ma, Junwei Deng, Qiaozhu Mei
Graph neural networks (GNNs) have attracted increasing interests. With broad deployments of GNNs in real-world applications, there is an urgent need for understanding the robustnes…
Subgroup Generalization and Fairness of Graph Neural Networks
Jiaqi Ma, Junwei Deng, Qiaozhu Mei
Despite enormous successful applications of graph neural networks (GNNs), theoretical understanding of their generalization ability, especially for node-level tasks where data are…
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
Xuan Lu, Wei Ai, Zhenpeng Chen +2
Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more em…
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks
Jiaqi Ma, Bo Chang, Xuefei Zhang +1
Graph-structured data are ubiquitous. However, graphs encode diverse types of information and thus play different roles in data representation. In this paper, we distinguish the \t…
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
Teng Ye, Wei Ai, Lingyu Zhang +4
Millions of drivers worldwide have enjoyed financial benefits and work schedule flexibility through a ride-sharing economy, but meanwhile they have suffered from the lack of a sens…
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks
Weijing Tang, Jiaqi Ma, Qiaozhu Mei +1
In this paper, we propose a flexible model for survival analysis using neural networks along with scalable optimization algorithms. One key technical challenge for directly applyin…
Neural Language Generation: Formulation, Methods, and Evaluation
Cristina Garbacea, Qiaozhu Mei
Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans and able to understand…
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Cristina Garbacea, Mengtian Guo, Samuel Carton +1
Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly genera…
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
Jiaqi Ma, Xinyang Yi, Weijing Tang +4
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint par…
Towards More Practical Adversarial Attacks on Graph Neural Networks
Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei
We study the black-box attacks on graph neural networks (GNNs) under a novel and realistic constraint: attackers have access to only a subset of nodes in the network, and they can…
Graph Representation Learning via Multi-task Knowledge Distillation
Jiaqi Ma, Qiaozhu Mei
Machine learning on graph structured data has attracted much research interest due to its ubiquity in real world data. However, how to efficiently represent graph data in a general…
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
Zhenpeng Chen, Yanbin Cao, Xuan Lu +2
Sentiment analysis has various application scenarios in software engineering (SE), such as detecting developers' emotions in commit messages and identifying their opinions on Q&A f…
A Flexible Generative Framework for Graph-based Semi-supervised Learning
Jiaqi Ma, Weijing Tang, Ji Zhu +1
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled sam…