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Vijay Prakash Dwivedi

11 accepted papers

2026

PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin +1
ICML 2026poster

Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train such models are rarely public due to privacy constraints. While there are methods to generate synthetic tabular data of…

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2026

Relational Graph Transformer

Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen, Federico Lopez, Charilaos I. Kanatsoulis, Rishi Puri +2
ICLR 2026poster

Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal graph. However, commonly used Graph Neural Network models suffer from fundamental limitations in capturing complex stru…

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2025

MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues

Kuluhan Binici, Abhinav Ramesh Kashyap, Viktor Schlegel, Andy T. Liu, Vijay Prakash Dwivedi, Thanh-Tung Nguyen +3
AAAI 2025technical

Automatic Speech Recognition (ASR) systems are pivotal in transcribing speech into text, yet the errors they introduce can significantly degrade the performance of downstream tasks like summarization. This issue is particularly pronounced in clinical dialogue summarization, a low-resource domain whe…

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2025

uMedSum: A Unified Framework for Clinical Abstractive Summarization

Aishik Nagar, Yutong Liu, Andy T. Liu, Viktor Schlegel, Vijay Prakash Dwivedi, Arun-Kumar Kaliya-Perumal +3
ACL 2025long

Clinical abstractive summarization struggles to balance faithfulness and informativeness, sacrificing key information or introducing confabulations. Techniques like in-context learning and fine-tuning have improved overall summary quality orthogonally, without considering the above issue. Conversely…

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2024

M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question Answering

Anand Subramanian, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler
ACL 2024findings

There is vivid research on adapting Large Language Models (LLMs) to perform a variety of tasks in high-stakes domains such as healthcare. Despite their popularity, there is a lack of understanding of the extent and contributing factors that allow LLMs to recall relevant knowledge and combine it with…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

Union Subgraph Neural Networks

Jiaxing Xu, Aihu Zhang, Qingtian Bian, Vijay Prakash Dwivedi, Yiping Ke
AAAI 2024technical

Graph Neural Networks (GNNs) are widely used for graph representation learning in many application domains. The expressiveness of vanilla GNNs is upper-bounded by 1-dimensional Weisfeiler-Leman (1-WL) test as they operate on rooted subtrees through iterative message passing. In this paper, we empowe…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2022

Graph Neural Networks with Learnable Structural and Positional Representations

Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson
ICLR 2022poster

Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical…

Cited by 448Source ↗PDF ↗code ↗Scholar ↗
2022

Long Range Graph Benchmark

Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu +1
NeurIPS 2022accept

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not able to capture long-range interactions (LRI) that may be desired or necessary for…

Cited by 255Source ↗PDF ↗code ↗Scholar ↗
2022

Recipe for a General, Powerful, Scalable Graph Transformer

Ladislav Rampasek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini
NeurIPS 2022accept

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph Transformers (GTs) have gained popularity in the field of graph representation learning with a variety of recent publica…

Cited by 690Source ↗PDF ↗code ↗Scholar ↗
2021

Self-critical Learning of Influencing Factors for Trajectory Prediction using Gated Graph Convolutional Network

Niraj Bhujel, Yau Wei Yun, Han Wang, Vijay Prakash Dwivedi
IROS 2021poster

Forecasting future trajectories of multiple pedestrians in a crowded environment is a challenging problem due to the complex interactions among the pedestrians. The interactions can be asymmetric and their influences may vary over time. Moreover, each pedestrian can exhibit different behavior at any…

Cited by 5Source ↗Scholar ↗