Shokri, R. and Shmatikov, V. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS '15, pp. 1310-1321, New York, NY, USA, 2015. Association for Computing Machinery. ISBN 9781450338325.
Snoek, J., Ovadia, Y., Fertig, E., Lakshminarayanan, B., Nowozin, S., Sculley, D., Dillon, J. V., Ren, J., and Nado, Z. Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pp. 13969-13980, 2019.
Tam, W. L., Liu, X., Ji, K., Xue, L., Zhang, X., Dong, Y., Liu, J., Hu, M., and Tang, J. Parameter-efficient prompt tuning makes generalized and calibrated neural text retrievers. arXiv preprint arXiv:2207.07087, 2022.
Varshney, V., Patidar, M., Kumar, R., Vig, L., and Shroff, G. Prompt augmented generative replay via supervised contrastive learning for lifelong intent detection. In *Findings of the Association for Computational Linguistics: NAACL* 2022, pp. 1113-1127, 2022.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. Attention is all you need. 2017.
Wang, J., Song, G., Wu, Y., and Wang, L. Streaming graph neural networks via continual learning. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM '20, pp. 1515-1524, New York, NY, USA, 2020. Association for Computing Machinery. ISBN 9781450368599.
Wang, Y., Chu, Z., Ouyang, X., Wang, S., Hao, H., Shen, Y., Gu, J., Xue, S., Zhang, J. Y., Cui, Q., et al. Enhancing recommender systems with large language model reasoning graphs. arXiv preprint arXiv:2308.10835, 2023.
Wang, Z., Zhang, Z., Ebrahimi, S., Sun, R., Zhang, H., Lee, C.-Y., Ren, X., Su, G., Perot, V., Dy, J., et al. Dualprompt: Complementary prompting for rehearsal-free continual learning. European Conference on Computer Vision, 2022a.
Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., and Pfister, T. Learning to prompt for continual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 139-149, 2022b.
Xiao, S., Yan, J., Yang, X., Zha, H., and Chu, S. Modeling the intensity function of point process via recurrent neural networks. In AAAI, 2017.
Xue, S., Shi, X., Hao, H., Ma, L., Zhang, J., Wang, S., and Wang, S. A graph regularized point process model for event propagation sequence. In 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2021.
Xue, S., Shi, X., Zhang, Y. J., and Mei, H. Hypro: A hybridly normalized probabilistic model for long-horizon prediction of event sequences. In Advances in Neural Information Processing Systems, 2022.
Xue, S., Shi, X., Chu, Z., Wang, Y., Zhou, F., Hao, H., Jiang, C., Pan, C., Xu, Y., Zhang, J. Y., Wen, Q., Zhou, J., and Mei, H. Easytpp: Towards open benchmarking the temporal point processes. 2023.
Yang, C., Mei, H., and Eisner, J. Transformer embeddings of irregularly spaced events and their participants. 2022.
Yang, Y., Etesami, J., He, N., and Kiyavash, N. Online learning for multivariate hawkes processes. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
Zenke, F., Poole, B., and Ganguli, S. Continual learning through synaptic intelligence. In ICML, 2017.
Zhang, Q., Lipani, A., Kirnap, O., and Yilmaz, E. Self-attentive Hawkes process. 2020.
Zuo, S., Jiang, H., Li, Z., Zhao, T., and Zha, H. Transformer Hawkes process. In International Conference on Machine Learning, pp. 11692-11702. PMLR, 2020.