Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning-Reference-Cited by-同舟云学术

Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

Published:2024-08-24 Issue: Volume:34 Page:2991-3000
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Danqing¹^ORCID,Antoniades Antonis²^ORCID,Luong Kha-Dinh²^ORCID,Zhang Edwin³^ORCID,Kosan Mert⁴^ORCID,Li Jiachen²^ORCID,Singh Ambuj²^ORCID,Wang William Yang²^ORCID,Li Lei⁵^ORCID

Affiliation:

1. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA

2. University of California, Santa Barbara, Santa Barbara, CA, USA

3. Harvard University & Founding, Cambridge, MA, USA

4. University of California, Santa Barbara, Santa Barbara, USA

5. Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672045

Reference55 articles.

1. Counterfactual Graphs for Explainable Classification of Brain Networks

2. Yuntao Bai Andy Jones Kamal Ndousse Amanda Askell Anna Chen Nova DasSarma Dawn Drain Stanislav Fort Deep Ganguli Tom Henighan et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022).

3. Yuntao Bai Saurav Kadavath Sandipan Kundu Amanda Askell Jackson Kernion Andy Jones Anna Chen Anna Goldie Azalia Mirhoseini Cameron McKinnon et al. 2022. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 (2022).

4. Robust counterfactual explanations on graph neural networks;Bajaj Mohit;Advances in Neural Information Processing Systems,2021

5. G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins. 2012. Quantifying the chemical beauty of drugs. Nature chemistry 4, 2 (2012), 90--98.