[29] Hieu Pham, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. Efficient neural architecture search via parameter sharing. In Proceedings of the 35th International Conference on Machine Learning, 2018.
[30] Yijian Qin, Xin Wang, Peng Cui, and Wenwu Zhu. Gqnas: Graph q network for neural architecture search. In 21st IEEE International Conference on Data Mining, 2021.
[31] Yijian Qin, Xin Wang, Zeyang Zhang, and Wenwu Zhu. Graph differentiable architecture search with structure learning. In Advances in neural information processing systems, 2021.
[32] Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, and Wenwu Zhu. Graph neural architecture search under distribution shifts. In International Conference on Machine Learning, pages 18083-18095. PMLR, 2022.
[33] Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, and Wenwu Zhu. Nas-bench-graph: Benchmarking graph neural architecture search. Advances in Neural Information Processing Systems, 35:54-69, 2022.
[34] Dripta S Raychaudhuri, Yumin Suh, Samuel Schulter, Xiang Yu, Masoud Faraki, Amit K Roy-Chowdhury, and Manmohan Chandraker. Controllable dynamic multi-task architectures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10955-10964, 2022.
[35] Ann M Richard, Richard S Judson, Keith A Houck, Christopher M Grulke, Patra Volarath, Inthirany Thillainadarajah, Chihae Yang, James Rathman, Matthew T Martin, John F Wambaugh, et al. Toxcast chemical landscape: paving the road to 21st century toxicology. Chemical research in toxicology, 29(8):1225-1251, 2016.
[36] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4822-4829, 2019.
[37] Tianxiang Sun, Yunfan Shao, Xiaonan Li, Pengfei Liu, Hang Yan, Xipeng Qiu, and Xuanjing Huang. Learning sparse sharing architectures for multiple tasks. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 8936-8943, 2020.
[38] Ximeng Sun, Rameswar Panda, Rogerio Feris, and Kate Saenko. Adashare: Learning what to share for efficient deep multi-task learning. Advances in Neural Information Processing Systems, 33:8728-8740, 2020.
[39] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations, 2018.
[40] Xin Wang, Yudong Chen, and Wenwu Zhu. A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):4555-4576, 2021.
[41] Xin Wang, Zirui Pan, Yuwei Zhou, Hong Chen, Chendi Ge, and Wenwu Zhu. Curriculum co-disentangled representation learning across multiple environments for social recommendation. 2023.
[42] Lanning Wei, Huan Zhao, Quanming Yao, and Zhiqiang He. Pooling architecture search for graph classification. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, 2021.
[43] Jie Wu and Yanan Sun. Evolving deep parallel neural networks for multi-task learning. In International Conference on Algorithms and Architectures for Parallel Processing, pages 517-531. Springer, 2021.
[44] Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan. Nodeformer: A scalable graph structure learning transformer for node classification. Advances in Neural Information Processing Systems, 35:27387-27401, 2022.