Nikhil Rao

Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs

2021

Knowledge Graphs (KGs) are ubiquitous structures for information storage in several real-world applications such as web search, e-commerce, social networks, and biology. Querying KGs remains a foundational and challenging problem due to their size and …

Task-Agnostic Graph Explanations

2022 · arXiv (Cornell University)

Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. …

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation

2023 · arXiv (Cornell University)

Distributed training of GNNs enables learning on massive graphs (e.g., social and e-commerce networks) that exceed the storage and computational capacity of a single machine. To reach performance comparable to centralized training, distributed frameworks focus …

Automatic Pair Construction for Contrastive Post-training

2024

Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, Nikhil Rao. Findings of the Association for Computational Linguistics: NAACL 2024. 2024.

Collaborative filtering with graph information: consistency and scalable methods

2015

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is …