Heimann, Mark

8 publications

ICLR 2024 Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

WACV 2023 Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification Rakshith Subramanyam, Mark Heimann, T.S. Jayram, Rushil Anirudh, Jayaraman J. Thiagarajan

NeurIPSW 2023 Estimating Epistemic Uncertainty of Graph Neural Networks Using Stochastic Centering Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan

LoG 2023 On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, Danai Koutra

NeurIPS 2022 Analyzing Data-Centric Properties for Graph Contrastive Learning Puja Trivedi, Ekdeep S Lubana, Mark Heimann, Danai Koutra, Jayaraman Thiagarajan

NeurIPSW 2022 Modeling Hierarchical Topological Structure in Scientific Images with Graph Neural Networks Samuel Leventhal, Attila Gyulassy, Valerio Pascucci, Mark Heimann

NeurIPS 2020 Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra

ECML-PKDD 2019 Node2bits: Compact Time- and Attribute-Aware Node Representations for User Stitching Di Jin, Mark Heimann, Ryan A. Rossi, Danai Koutra