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Donald Loveland
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- affiliation: Lawrence Livermore National Laboratory, USA
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2020 – today
- 2024
- [i11]Donald Loveland, Danai Koutra:
Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks. CoRR abs/2410.04287 (2024) - [i10]Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju:
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank. CoRR abs/2410.23300 (2024) - 2023
- [c4]Donald Loveland, Rajmonda Caceres:
Network Design Through Graph Neural Networks: Identifying Challenges and Improving Performance. COMPLEX NETWORKS (1) 2023: 3-15 - [c3]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG 2023: 6 - [i9]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. CoRR abs/2306.05557 (2023) - [i8]Donald Loveland, Rajmonda Caceres:
Network Design through Graph Neural Networks: Identifying Challenges and Improving Performance. CoRR abs/2310.17100 (2023) - 2022
- [c2]Jiong Zhu, Junchen Jin, Donald Loveland, Michael T. Schaub, Danai Koutra:
How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. KDD 2022: 2637-2647 - [i7]Donald Loveland, Jiayi Pan, Aaresh Farrokh Bhathena, Yiyang Lu:
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing. CoRR abs/2201.03681 (2022) - [i6]Ioannis C. Tsaknakis, Bhavya Kailkhura, Sijia Liu, Donald Loveland, James Diffenderfer, Anna Maria Hiszpanski, Mingyi Hong:
Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning. CoRR abs/2206.02785 (2022) - [i5]Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods. CoRR abs/2207.04376 (2022) - 2021
- [j2]Phan Nguyen, Donald Loveland, Joanne Taery Kim, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han:
Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning. J. Chem. Inf. Model. 61(5): 2147-2158 (2021) - [i4]Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han:
Reliable Graph Neural Network Explanations Through Adversarial Training. CoRR abs/2106.13427 (2021) - 2020
- [j1]Donald Loveland, Bhavya Kailkhura, Piyush Karande, Anna M. Hiszpanski, Thomas Yong-Jin Han:
Automated Identification of Molecular Crystals' Packing Motifs. J. Chem. Inf. Model. 60(12): 6147-6154 (2020) - [i3]Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han:
Actionable Attribution Maps for Scientific Machine Learning. CoRR abs/2006.16533 (2020) - [i2]Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han:
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design. CoRR abs/2007.08631 (2020)
2010 – 2019
- 2019
- [c1]Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han:
Generative Counterfactual Introspection for Explainable Deep Learning. GlobalSIP 2019: 1-5 - [i1]Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han:
Generative Counterfactual Introspection for Explainable Deep Learning. CoRR abs/1907.03077 (2019)
Coauthor Index
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