Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers
References
- (2021) Stochastic dueling bandits with adversarial corruption. Feldman V, Ligett K, Sabato S, eds. Proc. 32nd Internat. Conf. Algorithmic Learn. Theory (PMLR), 217–248.Google Scholar
- (2017) Corralling a band of bandit algorithms. Conf. Learn. Theory.Google Scholar
- (2014) Bandits with concave rewards and convex knapsacks. EC’14 Proc. 15th ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 989–1006.Google Scholar
- (2015) Linear contextual bandits with knapsacks. Lee DD, von Luxburg U, Garnett R, Sugiyama M, Guyon I, eds. NIPS’16 Proc. 30th Conf. Neural Inform. Processing Systems (NeurIPS) (Curran Associates, Red Hook, NY), 3458–3467.Google Scholar
- (2009) Dynamic pricing for nonperishable products with demand learning. Oper. Res. 57(5):1169–1188.Link, Google Scholar
- (2009) Minimax policies for adversarial and stochastic bandits. Proc. 22nd Annu. Conf. Learn. Theory COLT.Google Scholar
- (2014) Regret in online combinatorial optimization. Math. Oper. Res. 39(1):31–45.Link, Google Scholar
- (2018) Bandits with knapsacks. J. ACM 65(3):1–55.Crossref, Google Scholar
- (2017) Personalized dynamic pricing with machine learning. Preprint, submitted May 25, https://dx.doi.org/10.2139/ssrn.2972985.Google Scholar
- (2009) Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6):1407–1420.Link, Google Scholar
- (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.Link, Google Scholar
- (2015) Non-stationary stochastic optimization. Oper. Res. 63(5):1227–1244.Link, Google Scholar
- (2003) An overview of pricing models for revenue management. Manufacturing Service Oper. Management 5(3):203–229.Link, Google Scholar
- (2020) Corruption-tolerant Gaussian process bandit optimization. Internat. Conf. Artificial Intelligence Statist. AISTATS.Google Scholar
- (2012) Dynamic pricing under a general parametric choice model. Oper. Res. 60(4):965–980.Link, Google Scholar
- (2017) Kernel-based methods for bandit convex optimization. STOC 2017 Proc. 49th Annu. ACM SIGACT Sympos. Theory Comput. (Association for Computing Machinery, New York), 72–85.Google Scholar
- (2021) Nonparametric learning and optimization with covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
- (2019) Network revenue management with online inverse batch gradient descent method. Preprint, submitted February 10, https://dx.doi.org/10.2139/ssrn.3331939.Google Scholar
- (2016) A general decision theory for Huber’s ϵ-contamination model. Electronic J. Statist. 10(2):3752–3774.Crossref, Google Scholar
- (2015) Real-time dynamic pricing with minimal and flexible price adjustment. Management Sci. 62(8):2437–2455.Link, Google Scholar
- (2019) Robust dynamic assortment optimization in the presence of outlier customers. Preprint, submitted October 9, https://arxiv.org/abs/1910.04183.Google Scholar
- (2021a) Differential privacy in personalized pricing with nonparametric demand models. Preprint, submitted September 10, https://arxiv.org/abs/2109.04615.Google Scholar
- (2022) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. Forthcoming.Google Scholar
- (2021b) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Google Scholar
- (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, Google Scholar
- (2018) Hedging the drift: Learning to optimize under non-stationarity. Preprint, submitted October 5, https://dx.doi.org/10.2139/ssrn.3261050.Google Scholar
- (2006) Models of the spiral-down effect in revenue management. Oper. Res. 54(5):968–987.Link, Google Scholar
- (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.Crossref, Google Scholar
- (2013) Simultaneously learning and optimizing using controlled variance pricing. Management Sci. 60(3):770–783.Link, Google Scholar
- (2017) Being robust (in high dimensions) can be practical. Precup D, Teh YW, eds. ICML’17 Proc. 34th Internat. Conf. Machine Learn.(JMLR.org), 999–1008.Google Scholar
- (2018) Robustly learning a Gaussian: Getting optimal error, efficiently. Proc. ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 2683–2702.Google Scholar
- (2003) Dynamic pricing in the presence of inventory considerations: Research overview, current practices, and future directions. Management Sci. 49(10):1287–1309.Link, Google Scholar
- (2018) Allocation with traffic spikes: Mixing adversarial and stochastic models. ACM Trans. Econom. Comput. 6(3–4):1–23.Crossref, Google Scholar
- (2010) Dynamic pricing with a prior on market response. Oper. Res. 58(1):16–29.Link, Google Scholar
- (2018) Online network revenue management using Thompson sampling. Oper. Res. 66(6):1586–1602.Link, Google Scholar
- (2004) Online convex optimization in the bandit setting: Gradient descent without a gradient. SODA’05 Proc. Annu. ACM-SIAM Sympos. Discrete Algorithms (Society for Industrial and Applied Mathematics, Philadelphia), 385–394.Google Scholar
- (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020.Link, Google Scholar
- (1997) A multiproduct dynamic pricing problem and its applications to network yield management. Oper. Res. 45(1):24–41.Link, Google Scholar
- (2019) Incentive-aware contextual pricing with non-parametric market noise. Preprint, submitted November 8, https://arxiv.org/abs/1911.03508.Google Scholar
- (2020) Learning product rankings robust to fake users. Preprint, submitted September 2, https://dx.doi.org/10.2139/ssrn.3685465.Google Scholar
- (2019) Better algorithms for stochastic bandits with adversarial corruptions. Proc. Conf. Learn. Theory.Google Scholar
- (2012) Bayesian dynamic pricing policies: Learning and earning under a binary prior distribution. Management Sci. 58(3):570–586.Link, Google Scholar
- (2014) Bandit convex optimization: Toward tight bounds. Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, eds. Adv. Neural Inform. Processing Systems 27 NIPS 2014 (Curran Associates, Red Hook, NY).Google Scholar
- (1964) Robust estimation of a location parameter. Ann. Math. Statist. 35(1):73–101.Crossref, Google Scholar
- (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(9):1–49.Google Scholar
- (2020) Simultaneously learning stochastic and adversarial episodic MDPs with known transition. Proc. 34th Conf. Neural Inform. Processing Systems NeurIPS.Google Scholar
- (2014) Dynamic pricing with an unknown demand model: Asymptotically optimal semi-myopic policies. Oper. Res. 62(5):1142–1167.Link, Google Scholar
- (2016) Chasing demand: Learning and earning in a changing environment. Math. Oper. Res. 42(2):277–307.Link, Google Scholar
- (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions. Proc. 44th Annu. IEEE Sympos. Foundations Comput. Sci. FOCS. (IEEE, Piscataway, NJ), 594–605.Google Scholar
- (2021) Contextual search in the presence of irrational agents. STOC 2021 Proc. 53rd Annu. ACM SIGACT Sympos. Theory Comput. (Association for Computing Machinery, New York), 910–918.Google Scholar
- (2014) Near-optimal bisection search for nonparametric dynamic pricing with inventory constraint. Preprint, submitted October 1, https://dx.doi.org/10.2139/ssrn.2509425.Google Scholar
- (2018) Multidimensional binary search for contextual decision-making. Oper. Res. 66(5):1346–1361.Link, Google Scholar
- (2018) Stochastic bandits robust to adversarial corruptions. STOC 2018 Proc. 50th Annu. ACM SIGACT Sympos. Theory Comput. (Association for Computing Machinery, New York), 114–122.Google Scholar
- (2019) Corruption robust exploration in episodic reinforcement learning. Preprint, submitted November 20, https://arxiv.org/abs/1911.08689.Google Scholar
- (2019) Context–based dynamic pricing with online clustering. Preprint, submitted February 17, https://arxiv.org/abs/1902.06199.Google Scholar
- (2019) Dynamic learning and price optimization with endogeneity effect. Management Sci. 65(11):4980–5000.Link, Google Scholar
- (2018) Effort allocation and statistical inference for 1-dimensional multistart stochastic gradient descent. 2018 Winter Simulation Conf. (IEEE, Piscataway, NJ), 1850–1861.Google Scholar
- (2014) Close the gaps: A learning-while-doing algorithm for single-product revenue management problems. Oper. Res. 62(2):318–331.Link, Google Scholar
- (2021) Uncertainty quantification for demand prediction in contextual dynamic pricing. Production Oper. Management 30(6):1703–1717.Crossref, Google Scholar
- (2021) Tsallis-INF: An optimal algorithm for stochastic and adversarial bandits. J. Machine Learn. Res. 22(28):1–49.Google Scholar
- (2019) Beating stochastic and adversarial semi-bandits optimally and simultaneously. ICML Proc. Internat. Conf. Machine Learn.Google Scholar

