Math @ Duke

Papers Published
 Arlotto, A; Steele, JM, A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays,
Methodology and Computing in Applied Probability, vol. 20 no. 3
(September, 2018),
pp. 839854 [doi] [abs]
 Arlotto, A; Wei, Y; Xie, X, An adaptive O(log n)optimal policy for the online selection of a monotone subsequence from a random sample,
Random Structures & Algorithms, vol. 52 no. 1
(January, 2018),
pp. 4153, Wiley [doi] [abs]
 Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem.,
Corr, vol. abs/1809.02016
(2018)
 Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks,
Management Science
(2018), INFORMS
 Arlotto, A; Gurvich, I, Uniformly bounded regret in the multisecretary problem
(October, 2017) [abs]
 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. 14481468, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
 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. 235252, WILEY [doi]
 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. 21412168, Institute of Mathematical Statistics [doi]
 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
(September, 2015),
pp. 35963622, Elsevier BV [doi]
 Arlotto, A; Gans, N; Steele, JM, Markov Decision Problems Where Means Bound Variances,
Operations Research, vol. 62 no. 4
(August, 2014),
pp. 864875, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
 Arlotto, A; Steele, JM, Optimal Online Selection of an Alternating Subsequence: A Central Limit Theorem,
Advances in Applied Probability, vol. 46 no. 02
(June, 2014),
pp. 536559, Cambridge University Press (CUP) [doi] [abs]
 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. 110129, Institute for Operations Research and the Management Sciences (INFORMS) [doi]
 Arlotto, A; Chen, RW; Shepp, LA; Steele, JM, Online Selection of Alternating Subsequences from a Random Sample,
Journal of Applied Probability, vol. 48 no. 04
(December, 2011),
pp. 11141132, Cambridge University Press (CUP) [doi] [abs]
 ARLOTTO, ALESSANDRO; STEELE, JMICHAEL, Optimal Sequential Selection of a Unimodal Subsequence of a Random Sequence,
Combinatorics, Probability and Computing, vol. 20 no. 06
(November, 2011),
pp. 799814, Cambridge University Press (CUP) [doi] [abs]
 Arlotto, A; Gans, N; Chick, S, Optimal employee retention when inferring unknown learning curves, edited by Johansson, B; Jain, S; MontoyaTorres, J; Hugan, J; Yücesan, E,
Proceedings of the 2010 Winter Simulation Conference
(December, 2010),
pp. 11781188, IEEE [doi] [abs]
 Arlotto, A; Scarsini, M, Hessian orders and multinormal distributions,
Journal of Multivariate Analysis, vol. 100 no. 10
(November, 2009),
pp. 23242330, Elsevier BV [doi] [abs]


dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
 
Mathematics Department
Duke University, Box 90320
Durham, NC 277080320

