Kassraie, P. and Krause, A. (2022). Neural contextual bandits without regret. In International Conference on Artificial Intelligence and Statistics, pages 240-278. PMLR.
Lee, K., Kim, S.-A., Choi, J., and Lee, S.-W. (2018). Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling. In International Conference on Machine Learning., pages 2937-2946. PMLR.
Li, Z. and Scarlett, J. (2022). Gaussian process bandit optimization with few batches. In International Conference on Artificial Intelligence and Statistics.
Mercer, J. (1909). Functions of positive and negative type, and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 209:415-446.
Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E., Wang, S., Lee, Y.-J., Johnson, E., Pathak, O., Nazi, A., et al. (2021). A graph placement methodology for fast chip design. Nature, 594(7862):207-212.
Neu, G. and Pike-Burke, C. (2020). A unifying view of optimism in episodic reinforcement learning. Advances in Neural Information Processing Systems, 33:1392-1403.
Pozrikidis, C. (2014). An introduction to grids, graphs, and networks. Oxford University Press.
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Russo, D. (2019). Worst-case regret bounds for exploration via randomized value functions. Advances in Neural Information Processing Systems, 32.
Salgia, S., Vakili, S., and Zhao, Q. (2021). A domain-shrinking based Bayesian optimization algorithm with order-optimal regret performance. Conference on Neural Information Processing Systems, 34.
Scarlett, J., Bogunovic, I., and Cevher, V. (2017). Lower bounds on regret for noisy Gaussian process bandit optimization. In Conference on Learning Theory, pages 1723-1742. PMLR.
Scholkopf, B., Smola, A. J., Bach, F., et al. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484-489.
Srinivas, N., Krause, A., Kakade, S., and Seeger, M. (2010a). Gaussian process optimization in the bandit setting: No regret and experimental design. In ICML 2010 - Proceedings, 27th International Conference on Machine Learning, pages 1015-1022.
Srinivas, N., Krause, A., Kakade, S., and Seeger, M. (2010b). Gaussian process optimization in the bandit setting: no regret and experimental design. In Proceedings of the 27th International Conference on International Conference on Machine Learning, pages 1015-1022.
Vakili, S., Bouziani, N., Jalali, S., Bernacchia, A., and Shiu, D.-s. (2021a). Optimal order simple regret for Gaussian process bandits. Advances in Neural Information Processing Systems, 34:21202-21215.
Vakili, S., Bromberg, M., Garcia, J., Shiu, D.-s., and Bernacchia, A. (2021b). Uniform generalization bounds for overparameterized neural networks. arXiv preprint arXiv:2109.06099.
Vakili, S., Khezeli, K., and Picheny, V. (2021c). On information gain and regret bounds in Gaussian process bandits. In International Conference on Artificial Intelligence and Statistics, pages 82-90. PMLR.
Vakili, S., Scarlett, J., and Javidi, T. (2021d). Open problem: Tight online confidence intervals for RKHS elements. In Conference on Learning Theory, pages 4647-4652. PMLR.