Publications of Alessandro Arlotto    :chronological  alphabetical  combined  bibtex listing:

Papers Published

1. Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem with equal rewards, Stochastic Systems, vol. 10 no. 2 (June, 2020), pp. 170-191 [doi]  [abs]
2. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS
3. Arlotto, A; Gurvich, I, Uniformly bounded regret in the multi-secretary problem (October, 2017)  [abs]
4. Arlotto, A; Steele, JM, A central limit theorem for temporally nonhomogenous Markov chains with applications to dynamic programming, Mathematics of Operations Research, vol. 41 no. 4 (November, 2016), pp. 1448-1468, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
5. Arlotto, A; Mossel, E; Steele, JM, Quickest online selection of an increasing subsequence of specified size, Random Structures & Algorithms, vol. 49 no. 2 (September, 2016), pp. 235-252, WILEY [doi]  [abs]
6. Arlotto, A; Steele, JM, Beardwood–Halton–Hammersley theorem for stationary ergodic sequences: a counterexample, The Annals of Applied Probability, vol. 26 no. 4 (August, 2016), pp. 2141-2168, Institute of Mathematical Statistics [doi]
7. Arlotto, A; Nguyen, VV; Steele, JM, Optimal online selection of a monotone subsequence: A central limit theorem, Stochastic Processes and Their Applications, vol. 125 no. 9 (July, 2015), pp. 3596-3622, Elsevier BV [doi]  [abs]
8. Arlotto, A; Gans, N; Steele, JM, Markov decision problems where means bound variances, Operations Research, vol. 62 no. 4 (August, 2014), pp. 864-875, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
9. Arlotto, A; Steele, JM, Optimal Online Selection of an Alternating Subsequence: A Central Limit Theorem, Advances in Applied Probability, vol. 46 no. 2 (June, 2014), pp. 536-559, Cambridge University Press (CUP) [doi]  [abs]
10. Arlotto, A; Chick, SE; Gans, N, Optimal hiring and retention policies for heterogeneous workers who learn, Management Science, vol. 60 no. 1 (January, 2014), pp. 110-129, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
11. Arlotto, A; Chen, RW; Shepp, LA; Steele, JM, Online Selection of Alternating Subsequences from a Random Sample, Journal of Applied Probability, vol. 48 no. 4 (December, 2011), pp. 1114-1132, Cambridge University Press (CUP) [doi]  [abs]
12. Arlotto, A; Steele, JM, Optimal sequential selection of a unimodal subsequence of a random sequence, Combinatorics, Probability and Computing, vol. 20 no. 06 (November, 2011), pp. 799-814, Cambridge University Press (CUP) [doi]  [abs]
13. Arlotto, A; Gans, N; Chick, S, Optimal employee retention when inferring unknown learning curves, edited by Johansson, B; Jain, S; Montoya-Torres, J; Hugan, J; Yücesan, E, Proceedings Winter Simulation Conference (2010), pp. 1178-1188, IEEE [doi]  [abs]
14. Arlotto, A; Scarsini, M, Hessian orders and multinormal distributions, Journal of Multivariate Analysis, vol. 100 no. 10 (November, 2009), pp. 2324-2330, Elsevier BV [doi]  [abs]