SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Ariu, K., Kato, M., Komiyama, J., McAlinn, K., and Qin, C. (2021). Policy choice and best arm identification: Asymptotic analysis of exploration sampling.
Audibert, J.-Y., Bubeck, S., and Munos, R. (2010). Best arm identification in multi-armed bandits. In  $COLT$ , pages 41-53. CiteSeer.
Azizi, M., Kveton, B., and Ghavamzadeh, M. (2022). Fixed-budget best-arm identification in structured bandits. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pages 2798-2804.
Barrier, A., Garivier, A., and Stoltz, G. (2022). On best-arm identification with a fixed budget in non-parametric multi-armed bandits. arXiv preprint arXiv:2210.00895.
Berge, C. (1997). Topological Spaces: including a treatment of multi-valued functions, vector spaces, and convexity. Courier Corporation.
Boyd, S., Boyd, S. P., and Vandenberghe, L. (2004). Convex optimization. Cambridge university press.
Bubeck, S., Wang, T., and Viswanathan, N. (2013). Multiple identifications in multi-armed bandits. In International Conference on Machine Learning, pages 258-265. PMLR.
Budhiraja, A. and Dupuis, P. (2019). Analysis and approximation of rare events. Representations and Weak Convergence Methods. Series Prob. Theory and Stoch. Modelling, 94.
Carpentier, A. and Locatelli, A. (2016). Tight (lower) bounds for the fixed budget best arm identification bandit problem. In Conference on Learning Theory, pages 590-604. PMLR.
Combes, R., Magureanu, S., and Proutiere, A. (2017). Minimal exploration in structured stochastic bandits. In Proc. of NeurIPS.
Degenne, R. (2023). On the existence of a complexity in fixed budget bandit identification. In Proc. of COLT.
Degenne, R. and Koolen, W. M. (2019). Pure exploration with multiple correct answers. In Proc. of NeurIPS.
Dembo, A. and Zeitouni, O. (2009). Large deviations techniques and applications, volume 38. Springer Science & Business Media.
Dupuis, P. and Ellis, R. S. (2011). A weak convergence approach to the theory of large deviations. John Wiley & Sons.
Ellis, R. S. (1984). Large deviations for a general class of random vectors. The Annals of Probability, 12(1):1-12.
Gabillon, V., Ghavamzadeh, M., and Lazaric, A. (2012). Best arm identification: A unified approach to fixed budget and fixed confidence. Advances in Neural Information Processing Systems, 25.
Garivier, A. and Kaufmann, E. (2016). Optimal best arm identification with fixed confidence. In Proc. of COLT.
Garivier, A., Menard, P., and Stoltz, G. (2019). Explore first, exploit next: The true shape of regret in bandit problems. Mathematics of Operations Research, 44(2):377-399.
Gartner, J. (1977). On large deviations from the invariant measure. Theory of Probability & Its Applications, 22(1):24-39.
Glynn, P. and Juneja, S. (2004). A large deviations perspective on ordinal optimization. In Proceedings of the 2004 Winter Simulation Conference, 2004., volume 1. IEEE.
Karnin, Z., Koren, T., and Somekh, O. (2013). Almost optimal exploration in multi-armed bandits. In International Conference on Machine Learning, pages 1238-1246. PMLR.
Kaufmann, E., Cappé, O., and Garivier, A. (2016). On the complexity of best-arm identification in multi-armed bandit models. JMLR.
Kaufmann, E. and Koolen, W. (2018). Mixture martingales revisited with applications to sequential tests and confidence intervals. arXiv preprint arXiv:1811.11419.
Komiyama, J. (2022). Bayes optimal algorithm is suboptimal in frequentist best arm identification. arXiv preprint arXiv:2202.05193.
Komiyama, J., Tsuchiya, T., and Honda, J. (2022). Minimax optimal algorithms for fixed-budget best arm identification. In Advances in Neural Information Processing Systems.
Lai, T. L. and Robbins, H. (1985). Asymptotically efficient adaptive allocation rules. Advances in applied mathematics.
Magureanu, S., Combes, R., and Proutiere, A. (2014). Lipschitz bandits: Regret lower bound and optimal algorithms. In Conference on Learning Theory, pages 975-999. PMLR.
Qin, C. (2022). Open problem: Optimal best arm identification with fixed-budget. In Conference on Learning Theory, pages 5650-5654. PMLR.
Russo, D. (2016). Simple bayesian algorithms for best arm identification. In Annual Conference on Learning Theory. PMLR.
Varadhan, S. S. (2016). Large deviations, volume 27. American Mathematical Soc.
Wang, P.-A., Ariu, K., and Proutiere, A. (2023). On uniformly optimal algorithms for best arm identification in two-armed bandits with fixed budget. arXiv preprint arXiv:2308.12000.
Wang, P.-A., Tzeng, R.-C., and Proutiere, A. (2021). Fast pure exploration via frank-wolfe. Advances in Neural Information Processing Systems, 34.
Yang, J. and Tan, V. (2022). Minimax optimal fixed-budget best arm identification in linear bandits. Advances in Neural Information Processing Systems, 35:12253-12266.