[16] Zhenyi Wang, Li Shen, Tiehang Duan, Donglin Zhan, Le Fang, and Mingchen Gao. Learning to learn and remember super long multi-domain task sequence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7982-7992, 2022.
[17] James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521-3526, 2017.
[18] Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. In International Conference on Machine Learning, pages 3987-3995. PMLR, 2017.
[19] David Lopez-Paz and Marc'Aurelio Ranzato. Gradient episodic memory for continual learning. Advances in neural information processing systems, 30, 2017.
[20] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. Advances in neural information processing systems, 30, 2017.
[21] Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang. Lifelong learning with dynamically expandable networks. arXiv preprint arXiv:1708.01547, 2017.
[22] Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell. Progress & compress: A scalable framework for continual learning. In International Conference on Machine Learning, pages 4528-4537. PMLR, 2018.
[23] Alex Nichol and John Schulman. Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2(3):4, 2018.
[24] Jonathan Baxter. A model of inductive bias learning. Journal of artificial intelligence research, 12:149-198, 2000.
[25] Ron Amit and Ron Meir. Meta-learning by adjusting priors based on extended pac-bayes theory. In International Conference on Machine Learning, pages 205-214, 2018.
[26] Qi Chen, Changjian Shui, and Mario Marchand. Generalization bounds for meta-learning: An information-theoretic analysis. Advances in Neural Information Processing Systems, 34, 2021.
[27] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[28] Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, and Massimiliano Pontil. Learning-to-learn stochastic gradient descent with biased regularization. In International Conference on Machine Learning, pages 1566–1575. PMLR, 2019.
[29] Pierre Alquier, Massimiliano Pontil, et al. Regret bounds for lifelong learning. In Artificial Intelligence and Statistics, pages 261-269. PMLR, 2017.
[30] Maria-Florina Balcan, Mikhail Khodak, and Ameet Talwalkar. Provable guarantees for gradient-based meta-learning. In International Conference on Machine Learning, pages 424–433. PMLR, 2019.
[31] Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar. Adaptive gradient-based meta-learning methods. arXiv preprint arXiv:1906.02717, 2019.
[32] James Harrison, Apoorva Sharma, Chelsea Finn, and Marco Pavone. Continuous meta-learning without tasks. Advances in neural information processing systems, 33:17571-17581, 2020.
[33] Krishnan Raghavan and Prasanna Balaprakash. Formalizing the generalization-forgetting tradeoff in continual learning. Advances in Neural Information Processing Systems, 34:17284-17297, 2021.