5 citations · 7 across the 8 of their papers we have counts for
9 papers
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
Fangxin Wang, Peyman Baghershahi, Langzhou He +3
Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for of…
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
Peyman Baghershahi, Fangxin Wang, Debmalya Mandal +1
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) re…
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models
Angelo Zangari, Peyman Baghershahi, Sourav Medya
Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require reasoning over explicit structu…
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context
Peyman Baghershahi, Gregoire Fournier, Pranav Nyati +1
Graph Neural Networks (GNNs) have emerged as powerful tools for learning over structured data, including text-attributed graphs (TAGs), which are common in domains such as citation…
Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting
Peyman Baghershahi, Sourav Medya
Prompt tuning has become a key mechanism for adapting pre-trained Graph Neural Networks (GNNs) to new downstream tasks. However, existing approaches are predominantly supervised, r…
Temporal Relation Extraction in Clinical Texts: A Span-based Graph Transformer Approach
Rochana Chaturvedi, Peyman Baghershahi, Sourav Medya +1
Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the…
Design Requirements for Human-Centered Graph Neural Network Explanations
Pantea Habibi, Peyman Baghershahi, Sourav Medya +1
Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However,…
Efficient Relation-aware Neighborhood Aggregation in Graph Neural Networks via Tensor Decomposition
Peyman Baghershahi, Reshad Hosseini, Hadi Moradi
Numerous Graph Neural Networks (GNNs) have been developed to tackle the challenge of Knowledge Graph Embedding (KGE). However, many of these approaches overlook the crucial role of…
Self-attention Presents Low-dimensional Knowledge Graph Embeddings for Link Prediction
Peyman Baghershahi, Reshad Hosseini, Hadi Moradi
A few models have tried to tackle the link prediction problem, also known as knowledge graph completion, by embedding knowledge graphs in comparably lower dimensions. However, the…