[9] Dirk van der Hoeven, Nikita Zhivotovskiy, and Nicoló Cesa-Bianchi. A regret-variance trade-off in online learning. In Proc. Conference on Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Nov. 2022.
[10] N. Cesa-Bianchi, G. Lugosi, and G. Stoltz. Minimizing regret with label efficient prediction. IEEE Transactions on Information Theory, 51(6):2152-2162, 2005.
[11] D. J. C. Mackay. Information-based objective functions for active data selection. Neural Computation, 4:698-714, 1991.
[12] D. Cohn, Z. Ghahramani, and M. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, pages 129–145, 1996.
[13] Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3):133-168, Aug. 1997.
[14] Rui M. Castro and Robert D. Nowak. Minimax bounds for active learning. Transactions on Information Theory, 54(5):2339-2353, Apr. 2008.
[15] N. Balcan, A. Beygelzimer, and J. Langford. Agnostic active learning. In Proc. International Conference on Machine Learning (ICML), Pittsburgh, PA, USA, June 2006.
[16] Alina Beygelzimer, Sanjoy Dasgupta, and John Langford. Importance weighted active learning. In Proc. International Conference on Machine Learning (ICML), Montreal, June 2009.
[17] R. Castro, R. Willett, and R. Nowak. Faster rates in regression via active learning. In Proc. Conference on Neural Information Processing Systems (NeurIPS), 2005. extended version available at http://homepages.cae.wisc.edu/~rcastro/ECE-05-3.pdf.
[18] S. Dasgupta, A. Kalai, and C. Monteleoni. Analysis of perceptron-based active learning. In Proc. Conference on Learning Theory (COLT), Bertinoro, Italy, June 2005.
[19] S. Dasgupta. Coarse sample complexity bounds for active learning. In Proc. Conference on Neural Information Processing (NeurIPS), Vancouver, Canada, Dec. 2005.
[20] M. F. Balcan, C. Berlind, A. Blum, E. Cohen, K. Patnaik, and L. Song. Active learning and best-response dynamics. In Proc. Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2014.
[21] P. Awasthi, M. Balcan, and P. M. Long. The power of localization for efficiently learning linear separators with noise. In Proc. Annual ACM Symposium on Theory of Computing (STOC), New York, NY, USA, June 2014.
[22] A. Krause and C. Guestrin. Nonmyopic active learning of Gaussian processes: An exploration-exploitation approach. In Proc. International Conference on Machine Learning (ICML), 2007.
[23] X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In Workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, ICML, Washington, D.C., USA, Aug. 2003.
[24] J. L. Williams, J. W. Fisher, and A. S. Willsky. Performance guarantees for information theoretic active inference. In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), San Juan, Puerto Rico, Mar. 2007.
[25] B. Chen, R. Castro, and A. Krause. Joint optimization and variable selection of high-dimensional gaussian processes. In Proc. International Conference on Machine Learning (ICML), Edinburgh, Scotland, July 2012.
[26] A. Epshteyn, A. Vogel, and G. DeJong. Active reinforcement learning. In Proc. International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
[27] Steve Hanneke. Theory of disagreement-based active learning. Foundations and Trends in Machine Learning, 7(2-3):131–309, 2014.