Distribution-Free Contextual Dynamic Pricing

References

• [1] Abbasi-Yadkori Y, Pál D, Szepesvári C (2011) Improved algorithms for linear stochastic bandits. Adv. Neural Inform. Processing Systems 24:2312–2320.Google Scholar
• [2] Agrawal S, Goyal N (2013) Thompson sampling for contextual bandits with linear payoffs. Proc. Internat. Conf. Machine Learn., 127–135.Google Scholar
• [3] Auer P, Cesa-Bianchi N, Fischer P (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learn. 47(2):235–256.Crossref, Google Scholar
• [4] Ban GY, Keskin NB (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Link, Google Scholar
• [5] Bastani H, Simchi-Levi D, Zhu R (2022) Meta dynamic pricing: Transfer learning across experiments. Management Sci. 68(3):1865–1881.Link, Google Scholar
• [6] Besbes O, Zeevi A (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.Link, Google Scholar
• [7] Besbes O, Zeevi A (2011) On the minimax complexity of pricing in a changing environment. Oper. Res. 59(1):66–79.Link, Google Scholar
• [8] Besbes O, Zeevi A (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.Link, Google Scholar
• [9] Bickel PJ, Klaassen CA, Ritov Y, Wellner JA (1998) Efficient and Adaptive Estimation for Semiparametric Models (Springer, New York).Google Scholar
• [10] Broder J, Rusmevichientong P (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.Link, Google Scholar
• [11] Bubeck S, Cesa-Bianchi N (2012) Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations Trends Machine Learn. 5(1):1–122.Google Scholar
• [12] Cesa-Bianchi N, Cesari T, Perchet V (2019) Dynamic pricing with finitely many unknown valuations. Proc. 30th Internat. Conf. Algorithmic Learn. Theory, 247–273.Google Scholar
• [13] Chen N, Gallego G (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
• [14] Chen N, Gallego G (2022) A primal–dual learning algorithm for personalized dynamic pricing with an inventory constraint. Math. Oper. Res. 47(4):2585–2613.Link, Google Scholar
• [15] Chen Y, Wen Z, Xie Y (2019) Dynamic pricing in an evolving and unknown marketplace. Preprint, submitted June 6, https://dx.doi.org/10.2139/ssrn.3382957.Google Scholar
• [16] Chen X, Owen Z, Pixton C, Simchi-Levi D (2022) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Link, Google Scholar
• [17] Cheung WC, Simchi-Levi D, Wang H (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
• [18] Cheung WC, Simchi-Levi D, Zhu R (2022) Hedging the drift: Learning to optimize under nonstationarity. Management Sci. 68(3):1696–1713.Link, Google Scholar
• [19] Chu W, Li L, Reyzin L, Schapire R (2011) Contextual bandits with linear payoff functions. Proc. Internat. Conf. Artificial Intelligence Statistics, 208–214.Google Scholar
• [20] Cohen MC, Lobel I, Paes Leme R (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.Link, Google Scholar
• [21] den Boer AV (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.Crossref, Google Scholar
• [22] den Boer AV (2015) Tracking the market: Dynamic pricing and learning in a changing environment. Eur. J. Oper. Res. 247(3):914–927.Crossref, Google Scholar
• [23] den Boer AV, Keskin NB (2020) Discontinuous demand functions: Estimation and pricing. Management Sci. 66(10):4516–4534.Link, Google Scholar
• [24] den Boer AV, Zwart B (2014) Simultaneously learning and optimizing using controlled variance pricing. Management Sci. 60(3):770–783.Link, Google Scholar
• [25] Fan J, Guo Y, Yu M (2022) Policy optimization using semiparametric models for dynamic pricing. J. Amer. Statist. Assoc. 1–29.Crossref, Google Scholar
• [26] Foster D, Rakhlin A (2020) Beyond UCB: Optimal and efficient contextual bandits with regression oracles. Proc. Internat. Conf. Machine Learn., 3199–3210.Google Scholar
• [27] Foster DJ, Gentile C, Mohri M, Zimmert J (2020) Adapting to misspecification in contextual bandits. Adv. Neural Inform. Processing Systems 33:11478–11489.Google Scholar
• [28] Golrezaei N, Jaillet P, Liang JCN (2019) Incentive-aware contextual pricing with non-parametric market noise. Preprint, submitted November 8, https://arxiv.org/abs/1911.03508.Google Scholar
• [29] Golrezaei N, Javanmard A, Mirrokni V (2021) Dynamic incentive-aware learning: Robust pricing in contextual auctions. Oper. Res. 69(1):297–314.Link, Google Scholar
• [30] Huang J, Mani A, Wang Z (2022) The value of price discrimination in large social networks. Management Sci. 68(6):4454–4477.Link, Google Scholar
• [31] Javanmard A (2017) Perishability of data: Dynamic pricing under varying-coefficient models. J. Machine Learn. Res. 18(1):1714–1744.Google Scholar
• [32] Javanmard A, Nazerzadeh H (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
• [33] Javanmard A, Nazerzadeh H, Shao S (2020) Multi-product dynamic pricing in high-dimensions with heterogeneous price sensitivity. Proc. IEEE Internat. Sympos. Inform. Theory, 2652–2657.Google Scholar
• [34] Kallus N, Zhou A (2021) Fairness, welfare, and equity in personalized pricing. Proc. Conf. Fairness Accountability Transparency, 296–314.Google Scholar
• [35] Keskin NB, Zeevi A (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.Link, Google Scholar
• [36] Keskin NB, Zeevi A (2017) Chasing demand: Learning and earning in a changing environment. Math. Oper. Res. 42(2):277–307.Link, Google Scholar
• [37] Kleinberg R, Leighton T (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions. Proc. IEEE Sympos. Foundations Comput. Sci. (IEEE, Piscataway, NJ), 594–605.Google Scholar
• [38] Lattimore T, Szepesvári C (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• [39] Lattimore T, Szepesvari C, Weisz G (2020) Learning with good feature representations in bandits and in RL with a generative model. Proc. Internat. Conf. Machine Learn., 5662–5670.Google Scholar
• [40] Mao J, Leme R, Schneider J (2018) Contextual pricing for Lipschitz buyers. Adv. Neural Inform. Processing Systems 31:5643–5651.Google Scholar
• [41] Misra K, Schwartz EM, Abernethy J (2019) Dynamic online pricing with incomplete information using multiarmed bandit experiments. Marketing Sci. 38(2):226–252.Link, Google Scholar
• [42] Mueller JW, Syrgkanis V, Taddy M (2019) Low-rank bandit methods for high-dimensional dynamic pricing. Adv. Neural Inform. Processing Systems 32:15442–15452.Google Scholar
• [43] Nambiar M, Simchi-Levi D, Wang H (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.Link, Google Scholar
• [44] Pacchiano A, Phan M, Abbasi Yadkori Y, Rao A, Zimmert J, Lattimore T, Szepesvari C (2020) Model selection in contextual stochastic bandit problems. Adv. Neural Inform. Processing Systems 33:10328–10337.Google Scholar
• [45] Perchet V, Rigollet P (2013) The multi-armed bandit problem with covariates. Ann. Statist. 41(2):693–721.Crossref, Google Scholar
• [46] Phillips R, Şimşek AS, Van Ryzin G (2015) The effectiveness of field price discretion: Empirical evidence from auto lending. Management Sci. 61(8):1741–1759.Link, Google Scholar
• [47] Qiang S, Bayati M (2016) Dynamic pricing with demand covariates. Preprint, submitted April 25, https://arxiv.org/abs/1604.07463.Google Scholar
• [48] Russac Y, Vernade C, Cappé O (2019) Weighted linear bandits for non-stationary environments. Adv. Neural Inform. Processing Systems 32:12017–12026.Google Scholar
• [49] Shah V, Johari R, Blanchet J (2019) Semi-parametric dynamic contextual pricing. Adv. Neural Inform. Processing Systems 32:2363–2373.Google Scholar
• [50] Wang J, Shen X, Liu Y (2008) Probability estimation for large-margin classifiers. Biometrika. 95(1):149–167.Crossref, Google Scholar
• [51] Wang Y, Chen B, Simchi-Levi D (2021) Multimodal dynamic pricing. Management Sci. 67(10):6136–6152.Link, Google Scholar
• [52] Wang Z, Deng S, Ye Y (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems. Oper. Res. 62(2):318–331.Link, Google Scholar
• [53] Wang Y, Chen X, Chang X, Ge D (2021) Uncertainty quantification for demand prediction in contextual dynamic pricing. Production Oper. Management 30(6):1703–1717.Crossref, Google Scholar
• [54] Xu J, Wang YX (2021) Logarithmic regret in feature-based dynamic pricing. Adv. Neural Inform. Processing Systems 34:13898–13910.Google Scholar
• [55] Xu J, Wang YX (2022) Toward agnostic feature-based dynamic pricing: Linear policies vs linear valuation with unknown noise. Proc. Internat. Conf. Artificial Intelligence Statistics, 9643–9662.Google Scholar
• [56] Zhao P, Zhang L, Jiang Y, Zhou ZH (2020) A simple approach for non-stationary linear bandits. Proc. Internat. Conf. Artificial Intelligence Statistics, 746–755.Google Scholar