[2] A. M. Alaa and M. van der Schaar. Balancing suspense and surprise: Timely decision making with endogenous information acquisition. Advances in Neural Information Processing Systems, 29, 2016.
[3] W. Bao, F. Xie, C. Zuo, Y. Guan, and Y. H. Huang. Pet neuroimaging of alzheimer's disease: radiotracers and their utility in clinical research. Frontiers in Aging Neuroscience, 13:624330, 2021.
[4] L. Brunke, M. Greeff, A. W. Hall, Z. Yuan, S. Zhou, J. Panerati, and A. P. Schoellig. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems, 5:411-444, 2022.
[5] H. Chernoff. Sequential design of experiments. The Annals of Mathematical Statistics, 30(3): 755-770, 1959.
[6] S. Dayanik and A. J. Yu. Reward-rate maximization in sequential identification under a stochastic deadline. SIAM Journal on Control and Optimization, 51(4):2922-2948, 2013.
[7] E. Dolgin. Alzheimer's disease is getting easier to spot. Nature, 559(7715):S10-S10, 2018.
[8] B. Dubois, A. Padovani, P. Scheltens, A. Rossi, and G. Dell'Agnello. Timely diagnosis for alzheimer's disease: a literature review on benefits and challenges. Journal of Alzheimer's disease, 49(3):617-631, 2016.
[9] D. Jarrett and M. van der Schaar. Inverse active sensing: Modeling and understanding timely decision-making. In International Conference on Machine Learning, 2020.
[10] J. Jing, F. Zhang, L. Zhao, J. Xie, J. Chen, R. Zhong, Y. Zhang, and C. Dong. Correlation between brain 18f-av45 and 18f-fdg pet distribution characteristics and cognitive function in patients with mild and moderate alzheimer's disease. Journal of Alzheimer's Disease, 79(3): 1317-1325, 2021.
[11] P. Kidger, J. Morrill, J. Foster, and T. Lyons. Neural controlled differential equations for irregular time series. Advances in Neural Information Processing Systems, 33:6696-6707, 2020.
[12] J. Kossen, C. Cangea, E. Vértes, A. Jaegle, V. Patraucean, I. Ktena, N. Tomasev, and D. Belgrave. Active acquisition for multimodal temporal data: A challenging decision-making task. arXiv preprint arXiv:2211.05039, 2022.
[13] S. M. Lundberg and S.-I. Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017.
[14] N. Mastronarde and M. van der Schaar. Joint physical-layer and system-level power management for delay-sensitive wireless communications. IEEE transactions on mobile computing, 12(4): 694-709, 2012.
[15] M. Naghshvar and T. Javidi. Active sequential hypothesis testing. The Annals of Statistics, 41 (6):2703-2738, 2013.
[16] S. E. O'Bryant, S. C. Waring, C. M. Cullum, J. Hall, L. Lacritz, P. J. Massman, P. J. Lupo, J. S. Reisch, R. Doody, T. A. R. Consortium, et al. Staging dementia using clinical dementia rating scale sum of boxes scores: a Texas alzheimer's research consortium study. Archives of neurology, 65(8):1091-1095, 2008.
[17] S. E. O'Bryant, L. H. Lacritz, J. Hall, S. C. Waring, W. Chan, Z. G. Khodr, P. J. Massman, V. Hobson, and C. M. Cullum. Validation of the new interpretive guidelines for the clinical dementia rating scale sum of boxes score in the national alzheimer's coordinating center database. Archives of neurology, 67(6):746-749, 2010.
[18] L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta. Robust adversarial reinforcement learning. In International Conference on Machine Learning, pages 2817-2826. PMLR, 2017.
[19] M. T. Ribeiro, S. Singh, and C. Guestrin. "why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135-1144, 2016.