Thompson Sampling with Information Relaxation Penalties
References
- Agrawal S, Goyal N (2017) Near-optimal regret bounds for Thompson sampling. J. ACM 64:1–24.Google Scholar
- (1985) Bandit Problems: Sequential Allocation of Experiments , Monographs on Statistics and Applied Probability (Chapman & Hall, London).Crossref, Google Scholar
- (1956) On sequential designs for maximizing the sum of n observations. Ann. Math. Statist. 27(4):1060–1074.Crossref, Google Scholar
- (2017) Information relaxation bounds for infinite horizon Markov decision processes. Oper. Res. 65(5):1355–1379.Link, Google Scholar
- (2020) Index policies and performance bounds for dynamic selection problems. Management Sci. 66(7):3029–3050.Link, Google Scholar
- (2010) Information relaxations and duality in stochastic dynamic programs. Oper. Res. 58(4):785–801.Link, Google Scholar
- (2013) Prior-free and prior-dependent regret bounds for Thompson sampling. Burges CJ, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 26 (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (1994) A deterministic approach to optimal stopping. Kelly FP , ed. Probability, Statistics, and Optimisation (John Wiley & Sons Ltd., Hoboken, NJ), 455–466.Google Scholar
- (2012a) Bounds for Markov decision processes. Lewis FL , Liu D , eds. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (John Wiley & Sons, Inc., Hoboken, NJ), 452–473.Crossref, Google Scholar
- (2012b) Pathwise optimization for optimal stopping problems. Management Sci. 58(12):2292–2308.Link, Google Scholar
- (2013) Multi-armed bandit with budget constraint and variable costs. Proc. 27th AAAI Conf. Artificial Intelligence, vol. 27, no. 1 (AAAI Press, Palo Alto, CA), 232–238.Google Scholar
- Farias VF, Gutin E (2022) Optimistic Gittins indices. Oper. Res. 70(6):3432–3456.Google Scholar
- (1979) Bandit processes and dynamic allocation indices. J. Royal Statist. Soc. B 41(2):148–177.Crossref, Google Scholar
- (1995) Conjugate parameterizations for natural exponential families. J. Amer. Statist. Assoc. 90(432):1347–1356.Google Scholar
- (2004) Pricing American options: A duality approach. Oper. Res. 52(2):258–270.Link, Google Scholar
- (2019) Information relaxation bounds for partially observed Markov decision processes. IEEE Trans. Automatic Control 65(8):3256–3271.Crossref, Google Scholar
- (2012) Linear-quadratic control and information relaxations. Oper. Res. Lett. 40(6):521–528.Crossref, Google Scholar
- (2014) Dynamic portfolio execution and information relaxations. SIAM J. Financial Math. 5(1):316–359.Crossref, Google Scholar
- (2012a) On Bayesian upper confidence bounds for bandit problems. Lawrence ND, Girolami M, eds. Proc. 15th Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 22 (PMLR, New York), 592–600.Google Scholar
- (2012b) Thompson sampling: An asymptotically optimal finite-time analysis. Bshouty NH, Stoltz G, Vayatis N, Zeugmann T, eds. Proc. 23rd Internat. Conf. Algorithmic Learning Theory (Springer, Berlin, Heidelberg), 199–213.Google Scholar
- (1985) Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. 6(1):4–22.Crossref, Google Scholar
- (2016) Regret analysis of the finite-horizon Gittins index strategy for multi-armed bandits. Feldman V, Rakhlin A, Shamir O, eds. Proc. 29th Annu. Conf. Learn. Theory, Proceedings of Machine Learning Research, vol. 49 (PMLR, New York), 1214–1245.Google Scholar
- Min S, Maglaras C, Moallemi CC (2019) Thompson sampling with information relaxation penalties. Wallach H, Larochelle H, Beygelzimer A, Alché-Buc FD, Fox E, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 32 (Curran Associates Inc., Red Hook, NY).Google Scholar
- (2011) Computing a classic index for finite-horizon bandits. INFORMS J. Comput. 23(2):254–267.Link, Google Scholar
- (1991) Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. 16(1):119–147.Link, Google Scholar
- (2002) Monte Carlo valuation of American options. Math. Finance 12(3):271–286.Crossref, Google Scholar
- (2014) Learning to optimize via posterior sampling. Math. Oper. Res. 39(4):1221–1243.Link, Google Scholar
- (2018) Learning to optimize via information-directed sampling. Oper. Res. 66(1):230–252.Link, Google Scholar
- Russo D, Van Roy B (2022) Satisficing in time-sensitive bandit learning. Math. Oper. Res. 47(4):2815–2839.Google Scholar
- (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3–4):285–294.Crossref, Google Scholar

