Selection and Ordering Policies for Hiring Pipelines via Linear Programming

References

• Agrawal S, Sethuraman J, Zhang X (2020) On optimal ordering in the optimal stopping problem. Biró P, Hartline J, eds. Proc. 21st ACM Conf. on Econom. and Comput. (Association for Computing Machinery, New York), 187–188.Google Scholar
• Arnosti N, Ma W (2023) Tight guarantees for static threshold policies in the prophet secretary problem. Oper. Res. 71(5):1777–1788.Google Scholar
• Beyhaghi H, Golrezaei N, Leme RP, Pál M, Sivan B (2021) Improved revenue bounds for posted-price and second-price mechanisms. Oper. Res. 69(6):1805–1822.Link, Google Scholar
• Bradac D, Singla S, Zuzic G (2019) (Near) optimal adaptivity gaps for stochastic multi-value probing. Achlioptas D, Végh LA, eds. Approximation, Randomization, Combin. Optim. Algorithms Techniques, Leibniz International Proceedings in Informatics (LIPIcs), vol. 145 (Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Wadern), 1–49.Google Scholar
• Cominetti R, Correa JR, Rothvoß T, Martín JS (2010) Optimal selection of customers for a last-minute offer. Oper. Res. 58(4-part-1):878–888.Link, Google Scholar
• Esfandiari H, Hajiaghayi M, Liaghat V, Monemizadeh M (2017) Prophet secretary. SIAM J. Discrete Math. 31(3):1685–1701.Crossref, Google Scholar
• Fu H, Li J, Xu P (2018) A PTAS for a class of stochastic dynamic programs. Chatzigiannakis I, Kaklamanis C, Marx D, Sannella D, eds. 45th Internat. Colloquium Automation, Languages, Programming (ICALP 2018), Leibniz International Proceedings in Informatics (LIPIcs), vol. 107 (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Wadern, Germany), 1–56.Google Scholar
• Gallego G, Segev D (2022) A constructive prophet inequality approach to the adaptive probemax problem. Preprint, submitted October 14, https://arxiv.org/abs/2210.07556.Google Scholar
• Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics, vol. 209 (Springer, Berlin).Crossref, Google Scholar
• Gandhi R, Khuller S, Parthasarathy S, Srinivasan A (2006) Dependent rounding and its applications to approximation algorithms. J. ACM 53(3):324–360.Crossref, Google Scholar
• Goodrich MT, Tamassia R (2001) Algorithm Design: Foundations, Analysis, and Internet Examples (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Gupta A, Nagarajan V (2013) A Stochastic probing problem with applications. Goemans M, Correa J, eds. Integer Programming and Combinatorial Optimization. IPCO 2013, Lecture Notes in Computer Science, vol. 7801 (Springer, Berlin, Heidelberg).Google Scholar
• Gupta A, Nagarajan V, Singla S (2016) Algorithms and adaptivity gaps for stochastic probing. Krauthgamer R, ed. Proc. 27th Annual ACM-SIAM Sympos. on Discrete Algorithms (Association for Computing Machinery, New York), 1731–1747.Google Scholar
• Gupta A, Nagarajan V, Singla S (2017) Adaptivity gaps for stochastic probing: Submodular and xos functions. Krauthgamer R, ed. Proc. 28th Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 1688–1702.Google Scholar
• Hill T (1983) Prophet inequalities and order selection in optimal stopping problems. Proc. Amer. Math. Soc. 88(1):131–137.Crossref, Google Scholar
• Kleinberg R, Weinberg SM (2012) Matroid prophet inequalities. Karloff H, ed. Proc. 44th Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 123–136.Google Scholar
• Krengel U, Sucheston L (1977) Semiamarts and finite values. Bull. Amer. Math. Soc. (New Series) 83(4):745–747.Crossref, Google Scholar
• Krengel U, Sucheston L (1978) On semiamarts, amarts, and processes with finite value. Probability Banach Spaces 4:197–266.Google Scholar
• Purohit M, Gollapudi S, Raghavan M (2019) Hiring under uncertainty. Chaudhuri K, Salakhutdinov R, eds. Proc. Internat. Conf. Machine Learn. (PMLR, New York), 5181–5189.Google Scholar
• Samuel-Cahn E (1984) Comparison of threshold stop rules and maximum for independent nonnegative random variables. Annals Probability 12(4):1213–1216.Google Scholar
• Segev D, Singla S (2021) Efficient approximation schemes for stochastic probing and prophet problems. Biró P, ed. Proc. 22nd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 793–794.Google Scholar
• Yan Q (2011) Mechanism design via correlation gap. Randall D, ed. Proc. 22nd Annual ACM-SIAM Sympos. Discrete Algorithms (SIAM, Philadelphia), 710–719.Google Scholar