55 citations · 148 across the 12 of their papers we have counts for
12 papers
Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity
Dongyue Li, Aneesh Sharma, Hongyang R. Zhang
Multitask learning is a widely used paradigm for training models on diverse tasks, with applications ranging from graph neural networks to language model fine-tuning. Since tasks m…
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Dongyue Li, Haotian Ju, Aneesh Sharma +1
Predicting node labels on a given graph is a widely studied problem with many applications, including community detection and molecular graph prediction. This paper considers predi…
Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion
Haotian Ju, Dongyue Li, Aneesh Sharma +1
Graph neural networks are widely used tools for graph prediction tasks. Motivated by their empirical performance, prior works have developed generalization bounds for graph neural…
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community Labeling
Andrew Stolman, Caleb Levy, C. Seshadhri +1
Graph representation learning (also called graph embeddings) is a popular technique for incorporating network structure into machine learning models. Unsupervised graph embedding m…
The impossibility of low rank representations for triangle-rich complex networks
C. Seshadhri, Aneesh Sharma, Andrew Stolman +1
The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms fo…
LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew
Cyrus Rashtchian, Aneesh Sharma, David P. Woodruff
All-pairs set similarity is a widely used data mining task, even for large and high-dimensional datasets. Traditionally, similarity search has focused on discovering very similar p…