Micchelli, C. A. and Pontil, M. (2004). A function representation for learning in banach spaces. In Learning Theory: 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004. Proceedings 17, pages 255-269. Springer.
Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P. (2018). A simple neural attentive meta-learner. In International Conference on Learning Representations.
Nadaraya, E. A. (1964). On estimating regression. Theory of Probability & Its Applications, 9(1):141-142.
Nelsen, N. H. and Stuart, A. M. (2021). The random feature model for input-output maps between banach spaces. SIAM Journal on Scientific Computing, 43(5):A3212-A3243.
Olsson, C., Elhage, N., Nanda, N., Joseph, N., DasSarma, N., Henighan, T., Mann, B., Askell, A., Bai, Y., Chen, A., et al. (2022). In-context learning and induction heads. arXiv preprint arXiv:2209.11895.
Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., et al. (2019). Meta-learning of sequential strategies. arXiv preprint arXiv:1905.03030.
Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar, A. (2022). Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators.
Pickering, E., Guth, S., Karniadakis, G. E., and Sapsis, T. P. (2022). Discovering and forecasting extreme events via active learning in neural operators. Nature Computational Science, 2(12):823-833.
Quinlan, J. R. (1986). Induction of decision trees. Machine learning, 1(1):81-106.
Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2017). Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561.
Ravi, S. and Larochelle, H. (2017). Optimization as a model for few-shot learning. In *ICLR*.
Roberts, N. C., Khodak, M., Dao, T., Li, L., Re, C., and Talwalkar, A. (2021). Rethinking neural operations for diverse tasks. In Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems.
Santoro, A., Bartunov, S., Botvinick, M. M., Wierstra, D., and Lillicrap, T. P. (2016). One-shot learning with memory-augmented neural networks. CoRR, abs/1605.06065.
Schmidhuber, J., Zhao, J., and Wiering, M. (1997). Shifting inductive bias with success-story algorithm, adaptive levin search, and incremental self-improvement. Machine Learning, 28.
Snell, J., Swersky, K., and Zemel, R. (2017). Prototypical networks for few-shot learning. Advances in neural information processing systems, 30.
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M. (2018). Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Vapnik, V. (1999). The nature of statistical learning theory. Springer science & business media.
Vapnik, V. (2006). Estimation of dependences based on empirical data. Springer Science & Business Media.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I. (2017). Attention is all you need. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.